diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0ab9c0a..7396ea9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,10 +6,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 ### Added
-- Zenodo to automatically generate a DOI for new releases
+- Integration with Zenodo to automatically generate a DOI for new releases
 - Citation page
-- PDBQT section in the "How-to" notebook
-- Example PDBQT files from Vina in the data folder
+- Docking section in the Quickstart notebook (Issue #11)
+- PDBQT, MOL2 and SDF molecule suppliers to make it easier for users to use docking
+  results as input (Issue #11)
+- `Molecule.from_rdkit` classmethod to easily prepare RDKit molecules for ProLIF
 ### Changed
 - The visualisation notebook now displays the protein with py3Dmol. Some examples for
   creating and displaying a graph from the interaction dataframe have been added
@@ -17,6 +19,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - The previous repr method of `ResidueId` was easy to confuse with a string, especially
   when trying to access the `Fingerprint.ifp` results by string. The new repr method is
   now more explicit.
+- Added the `Fingerprint.run_from_iterable` method, which uses the new supplier functions
+  to quickly generate a fingerprint.
+- Sorted the output of `Fingerprint.list_available`
 ### Deprecated
 ### Removed
 ### Fixed
diff --git a/docs/notebooks/how-to.ipynb b/docs/notebooks/how-to.ipynb
index e64dc30..097dca0 100644
--- a/docs/notebooks/how-to.ipynb
+++ b/docs/notebooks/how-to.ipynb
@@ -6,7 +6,15 @@
    "source": [
     "# How-to\n",
     "\n",
-    "This notebook serves as a practical guide to common questions users might have."
+    "This notebook serves as a practical guide to common questions users might have.\n",
+    "\n",
+    "**Table of content**\n",
+    "\n",
+    "- [Changing the parameters for an interaction](#Changing-the-parameters-for-an-interaction)\n",
+    "- [Writing your own interaction](#Writing-your-own-interaction)\n",
+    "- [Working with docking poses instead of MD simulations](#Working-with-docking-poses-instead-of-MD-simulations)\n",
+    "- [Using PDBQT files](#Using-PDBQT-files)\n",
+    "\n"
    ]
   },
   {
@@ -17,6 +25,7 @@
    "source": [
     "import MDAnalysis as mda\n",
     "import prolif as plf\n",
+    "import pandas as pd\n",
     "import numpy as np"
    ]
   },
@@ -37,7 +46,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 1. Changing the parameters for an interaction"
+    "## Changing the parameters for an interaction"
    ]
   },
   {
@@ -81,7 +90,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 1.a Overwriting the original interaction"
+    "### Overwriting the original interaction"
    ]
   },
   {
@@ -136,7 +145,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 1.b Reparameterizing an interaction with another name"
+    "### Reparameterizing an interaction with another name"
    ]
   },
   {
@@ -183,7 +192,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 2. Writing your own interaction"
+    "## Writing your own interaction"
    ]
   },
   {
@@ -339,19 +348,17 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "metadata": {},
    "source": [
-    "## 3. Using PDBQT files\n",
+    "## Working with docking poses instead of MD simulations\n",
     "\n",
-    "The typical use case here is getting the IFP from AutoDock Vina's output. It requires a few additional steps and informations compared to other formats like MOL2, since the PDBQT format gets rid of most hydrogen atoms and doesn't contain bond order information.\n",
+    "ProLIF currently provides file readers for MOL2, SDF and PDBQT files. The API is slightly different compared to the quickstart example but the end result is the same.\n",
     "\n",
-    "The prerequisites for a successfull usage of ProLIF in this case is having external files (typically in the MOL2 format) that contain bond orders and formal charges for your ligand and protein, or at least a PDB file with explicit hydrogen atoms. \n",
+    "Please note that this part of the tutorial is only suitable for interactions between one protein and several ligands, or in more general terms, between one molecule with multiple residues and one molecule with a single residue. This is not suitable for protein-protein or DNA-protein interactions.\n",
     "\n",
-    "Please note that your PDBQT input must have a single model per file. Splitting a multi-model file can be done using the `vina_split` command-line tool that comes with AutoDock Vina: `vina_split --input vina_output.pdbqt`\n",
-    "\n",
-    "Let's start by loading our \"template\" file with bond orders. It can be a SMILES string, MOL2, SDF file or anything supported by RDKit."
-   ]
+    "Let's start by loading the protein. Here I'm using a PDB file but you can use any format supported by MDAnalysis as long as it contains explicit hydrogens."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
   },
   {
    "cell_type": "code",
@@ -359,25 +366,45 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from rdkit import Chem\n",
-    "from rdkit.Chem import AllChem\n",
-    "\n",
-    "template = Chem.MolFromSmiles(\"C[NH+]1CC(C(=O)NC2(C)OC3(O)C4CCCN4C(=O)C\"\n",
-    "                              \"(Cc4ccccc4)N3C2=O)C=C2c3cccc4[nH]cc(c34)CC21\")\n",
-    "template"
+    "# load protein\n",
+    "prot = mda.Universe(plf.datafiles.datapath / \"vina\" / \"rec.pdb\")\n",
+    "prot = plf.Molecule.from_mda(prot)\n",
+    "prot.n_residues"
    ]
   },
   {
+   "source": [
+    "### Using an SDF file"
+   ],
    "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "Next, we'll define a function that loads a PDBQT file and assigns bond orders and charges using the template. The template and  PDBQT file must have the exact same atoms (even hydrogens) otherwise no match will be found. Since PDBQT files partially keep the hydrogen atoms, we have the choice between:\n",
+    "# load ligands\n",
+    "path = str(plf.datafiles.datapath / \"vina\" / \"vina_output.sdf\")\n",
+    "lig_suppl = list(plf.sdf_supplier(path))\n",
+    "# generate fingerprint\n",
+    "fp = plf.Fingerprint()\n",
+    "fp.run_from_iterable(lig_suppl, prot)\n",
+    "df = fp.to_dataframe()\n",
+    "df"
+   ]
+  },
+  {
+   "source": [
+    "Please note that converting the `lig_suppl` to a list is optionnal (and maybe not suitable for large files) as it will load all the ligands in memory, but it's nicer to track the progression with the progress bar.\n",
     "\n",
-    "* Manually selecting where to add the hydrogens on the template, do the matching, then add the remaining hydrogens\n",
-    "* Or just remove the hydrogens from the PDBQT file, do the matching, then add all hydrogens.\n",
+    "If you want to calculate the Tanimoto similarity between your docked poses and a reference ligand, here's how to do it.\n",
     "\n",
-    "This last option will delete the coordinates of your hydrogens atoms and replace them by the ones generated by RDKit, but unless you're working with an exotic system this should be fine."
-   ]
+    "We first need to generate the interaction fingerprint for the reference, and concatenate it to the previous one"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
   },
   {
    "cell_type": "code",
@@ -385,32 +412,29 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mda_to_rdkit = mda._CONVERTERS[\"RDKIT\"]().convert\n",
-    "\n",
-    "def pdbqt_to_rdkit(pdbqt, template):\n",
-    "    pdbqt = mda.Universe(pdbqt)\n",
-    "    # set attributes needed by the converter\n",
-    "    elements = [mda.topology.guessers.guess_atom_element(x)\n",
-    "                for x in pdbqt.atoms.names]\n",
-    "    pdbqt.add_TopologyAttr(\"elements\", elements)\n",
-    "    pdbqt.add_TopologyAttr(\"chainIDs\", pdbqt.atoms.segids)\n",
-    "    pdbqt.atoms.types = pdbqt.atoms.elements\n",
-    "    # convert without infering bond orders and charges\n",
-    "    mol = mda_to_rdkit(pdbqt.atoms, NoImplicit=False)\n",
-    "    # assign BO from template then add hydrogens\n",
-    "    mol = Chem.RemoveHs(mol, sanitize=False)\n",
-    "    mol = AllChem.AssignBondOrdersFromTemplate(template, mol)\n",
-    "    mol = Chem.AddHs(mol, addCoords=True, addResidueInfo=True)\n",
-    "    return mol\n",
-    "\n",
-    "pdbqt_to_rdkit(plf.datafiles.datapath / \"vina/vina_output_ligand_1.pdbqt\", template)"
+    "# load the reference\n",
+    "ref = mda.Universe(plf.datafiles.datapath / \"vina\" / \"lig.pdb\")\n",
+    "ref = plf.Molecule.from_mda(ref)\n",
+    "# generate IFP\n",
+    "fp.run_from_iterable([ref], prot)\n",
+    "df0 = fp.to_dataframe()\n",
+    "df0.rename({0: \"ref\"}, inplace=True)\n",
+    "# drop the ligand level on both dataframes\n",
+    "df0.columns = df0.columns.droplevel(0)\n",
+    "df.columns = df.columns.droplevel(0)\n",
+    "# concatenate them\n",
+    "df = (pd.concat([df0, df])\n",
+    "        .fillna(False)\n",
+    "        .sort_index(axis=1, level=0,\n",
+    "                    key=lambda index: [plf.ResidueId.from_string(x) for x in index]))\n",
+    "df"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now our ligand is ready to be used. For the protein, there's usually no need to load the PDBQT that was used by Vina. The original file that was used to generate the PDBQT can be used directly, but **it must contain explicit hydrogen atoms**:"
+    "Lastly, we can convert the dataframe to a list of RDKit bitvectors to finally compute the Tanimoto similarity between our reference pose and the docking poses generated by Vina:"
    ]
   },
   {
@@ -419,51 +443,43 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "prot = mda.Universe(plf.datafiles.datapath / \"vina/rec.pdb\")\n",
-    "prot = plf.Molecule.from_mda(prot)\n",
-    "prot.GetNumHeavyAtoms()"
+    "from rdkit import DataStructs\n",
+    "\n",
+    "bvs = plf.to_bitvectors(df)\n",
+    "for i, bv in enumerate(bvs[1:]):\n",
+    "    tc = DataStructs.TanimotoSimilarity(bvs[0], bv)\n",
+    "    print(f\"{i}: {tc:.3f}\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We are now ready to compute the IFP for our docking poses (we will also do it for the reference structure):"
+    "Interestingly, the best scored docking pose (#0) isn't the most similar to the reference (#5)"
    ]
   },
+  {
+   "source": [
+    "### Using a MOL2 file\n",
+    "\n",
+    "The input mol2 file can contain multiple ligands in different conformations."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# load ligands\n",
+    "path = plf.datafiles.datapath / \"vina\" / \"vina_output.mol2\"\n",
+    "lig_suppl = list(plf.mol2_supplier(path))\n",
+    "# generate fingerprint\n",
     "fp = plf.Fingerprint()\n",
-    "ifp = []\n",
-    "\n",
-    "# equivalent to what happens in the fp.run method\n",
-    "def run_fp(lig, prot, frame=0):\n",
-    "    data = {\"Frame\": frame}\n",
-    "    for lres in lig:\n",
-    "        for presid in plf.get_residues_near_ligand(lres, prot):\n",
-    "            bv = fp.bitvector(lres, prot[presid])\n",
-    "            data[(lres.resid, presid)] = bv\n",
-    "    return data\n",
-    "\n",
-    "# original structure for reference\n",
-    "lig = mda.Universe(plf.datafiles.datapath / \"vina/lig.pdb\")\n",
-    "lig = plf.Molecule.from_mda(lig)\n",
-    "data = run_fp(lig, prot, frame=0)\n",
-    "ifp.append(data)\n",
-    "\n",
-    "# docking poses\n",
-    "pdbqt_files = sorted((plf.datafiles.datapath / \"vina\").glob(\"*.pdbqt\"))\n",
-    "for i, pdbqt in enumerate(pdbqt_files, start=1):\n",
-    "    lig = pdbqt_to_rdkit(pdbqt, template)\n",
-    "    lig = plf.Molecule(lig)\n",
-    "    data = run_fp(lig, prot, frame=i)\n",
-    "    ifp.append(data)\n",
-    "\n",
-    "df = plf.to_dataframe(ifp, fp.interactions.keys())\n",
+    "fp.run_from_iterable(lig_suppl, prot)\n",
+    "df = fp.to_dataframe()\n",
     "df"
    ]
   },
@@ -471,7 +487,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Lastly, we can now compute the Tanimoto similarity between our reference pose and the docking poses generated by Vina:"
+    "### Using PDBQT files\n",
+    "\n",
+    "The typical use case here is getting the IFP from AutoDock Vina's output. It requires a few additional steps and informations compared to other formats like MOL2, since the PDBQT format gets rid of most hydrogen atoms and doesn't contain bond order information.\n",
+    "\n",
+    "The prerequisites for a successfull usage of ProLIF in this case is having external files that contain bond orders and formal charges for your ligand (like SMILES, SDF or MOL2), or at least a file with explicit hydrogen atoms. \n",
+    "\n",
+    "Please note that your PDBQT input must have a single model per file (this is required by MDAnalysis). Splitting a multi-model file can be done using the `vina_split` command-line tool that comes with AutoDock Vina: `vina_split --input vina_output.pdbqt`\n",
+    "\n",
+    "Let's start by loading our \"template\" file with bond orders. It can be a SMILES string, MOL2, SDF file or anything supported by RDKit."
    ]
   },
   {
@@ -480,24 +504,26 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from rdkit import DataStructs\n",
+    "from rdkit import Chem\n",
+    "from rdkit.Chem import AllChem\n",
     "\n",
-    "bvs = plf.to_bitvectors(df)\n",
-    "DataStructs.BulkTanimotoSimilarity(bvs[0], bvs[1:])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Interestingly, the best scored docking pose (#1) isn't the most similar to the reference (#6)!"
+    "template = Chem.MolFromSmiles(\"C[NH+]1CC(C(=O)NC2(C)OC3(O)C4CCCN4C(=O)C\"\n",
+    "                              \"(Cc4ccccc4)N3C2=O)C=C2c3cccc4[nH]cc(c34)CC21\")\n",
+    "template"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 4. Ignoring the protein backbone when computing interactions"
+    "Next, we'll use the PDBQT supplier which loads each file from a list of paths, and assigns bond orders and charges using the template. The template and PDBQT file must have the exact same atoms, **even hydrogens**, otherwise no match will be found. Since PDBQT files partially keep the hydrogen atoms, we have the choice between:\n",
+    "\n",
+    "* Manually selecting where to add the hydrogens on the template, do the matching, then add the remaining hydrogens (not covered here)\n",
+    "* Or just remove the hydrogens from the PDBQT file, do the matching, then add all hydrogens.\n",
+    "\n",
+    "This last option will delete the coordinates of your hydrogens atoms and replace them by the ones generated by RDKit, but unless you're working with an exotic system this should be fine.\n",
+    "\n",
+    "For the protein, there's usually no need to load the PDBQT that was used by Vina. The original file that was used to generate the PDBQT can be used directly, but **it must contain explicit hydrogen atoms**:"
    ]
   },
   {
@@ -506,7 +532,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# not implemented yet"
+    "# load ligands\n",
+    "pdbqt_files = sorted(plf.datafiles.datapath.glob(\"vina/*.pdbqt\"))\n",
+    "lig_suppl = list(plf.pdbqt_supplier(pdbqt_files, template))\n",
+    "# generate fingerprint\n",
+    "fp = plf.Fingerprint()\n",
+    "fp.run_from_iterable(lig_suppl, prot)\n",
+    "df = fp.to_dataframe()\n",
+    "df"
    ]
   }
  ],
@@ -526,9 +559,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.5-final"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file
diff --git a/docs/notebooks/quickstart.ipynb b/docs/notebooks/quickstart.ipynb
index 9268388..f023186 100644
--- a/docs/notebooks/quickstart.ipynb
+++ b/docs/notebooks/quickstart.ipynb
@@ -232,7 +232,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can also compute a Tanimoto similarity between each frames:"
+    "You can also compute a Tanimoto similarity between each frame:"
    ]
   },
   {
@@ -269,4 +269,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file
diff --git a/prolif/__init__.py b/prolif/__init__.py
index ee651ad..d23658a 100644
--- a/prolif/__init__.py
+++ b/prolif/__init__.py
@@ -1,7 +1,12 @@
-from .molecule import Molecule
+from .molecule import (Molecule,
+                       pdbqt_supplier,
+                       mol2_supplier,
+                       sdf_supplier)
 from .residue import ResidueId
 from .fingerprint import Fingerprint
-from .utils import get_residues_near_ligand, to_dataframe, to_bitvectors
+from .utils import (get_residues_near_ligand,
+                    to_dataframe,
+                    to_bitvectors)
 from . import datafiles
 from ._version import get_versions
 __version__ = get_versions()['version']
diff --git a/prolif/data/vina/vina_output.mol2 b/prolif/data/vina/vina_output.mol2
new file mode 100644
index 0000000..5fe2274
--- /dev/null
+++ b/prolif/data/vina/vina_output.mol2
@@ -0,0 +1,1566 @@
+@<TRIPOS>MOLECULE
+
+79 86 1
+SMALL
+USER_CHARGES
+@<TRIPOS>ATOM
+1	C1     1.4240     1.3400    12.7270	C.3	1	noname	0.2017
+2	C2     2.4870     0.8460    13.7760	C.2	1	noname	0.0859
+3	C3     4.0730     1.9290    15.2560	C.3	1	noname	0.1001
+4	O1     2.0390     2.5820    12.2920	O.3	1	noname	-0.3049
+5	C4     3.9120     3.3800    15.9040	C.2	1	noname	0.0539
+6	C5     3.4980     5.8160    15.5500	C.3	1	noname	0.0065
+7	C6     2.6560     6.4710    14.4840	C.3	1	noname	-0.0381
+8	C7     2.6580     5.5380    13.2830	C.3	1	noname	-0.0291
+9	C8     2.7950     4.1300    14.0210	C.3	1	noname	0.0870
+10	C9     3.1900     2.9120    13.1250	C.3	1	noname	0.2535
+11	C10     0.2490     1.4570    13.7150	C.3	1	noname	-0.0125
+12	O2     2.4730    -0.2350    14.4210	O.2	1	noname	-0.2735
+13	N1     3.4540     1.8360    13.9640	N.3	1	noname	-0.0076
+14	O3     4.1160     3.4310    17.1140	O.2	1	noname	-0.2883
+15	N2     3.6390     4.3990    15.1010	N.3	1	noname	-0.0572
+16	N3     1.2210     0.6180    11.5910	N.3	1	noname	-0.0502
+17	C11     0.6920     1.1580    10.4110	C.2	1	noname	0.0544
+18	O4     0.3000     2.3070    10.3880	O.2	1	noname	-0.2899
+19	C12     0.7180     0.2040     9.2400	C.3	1	noname	0.0996
+20	C13     1.2190     1.0110     8.0210	C.3	1	noname	0.0041
+21	N4     1.2970     0.0640     6.8800	N.4	1	noname	0.0094
+22	C14     1.3330     0.9250     5.5640	C.3	1	noname	-0.0218
+23	C15     2.2380    -1.0390     6.9860	C.3	1	noname	0.0303
+24	C16     2.0880    -2.1970     5.9570	C.3	1	noname	0.0498
+25	C17     2.3040    -1.4620     8.5110	C.2	1	noname	-0.0158
+26	C18     3.1050    -3.3550     6.1960	C.2	1	noname	-0.0271
+27	C19     3.7040    -4.3130     5.5140	C.2	1	noname	-0.1133
+28	C20     3.5500    -3.3880     7.5780	C.2	1	noname	-0.0193
+29	N5     4.5340    -5.0360     6.4660	N.3	1	noname	-0.0790
+30	C21     4.3780    -4.5210     7.7400	C.2	1	noname	-0.0845
+31	C22     4.8510    -4.8830     8.9500	C.2	1	noname	-0.0420
+32	C23     4.5410    -4.0450    10.0910	C.2	1	noname	-0.0462
+33	C24     3.7890    -2.9380     9.9940	C.2	1	noname	-0.0383
+34	C25     3.2150    -2.6450     8.7010	C.2	1	noname	-0.0179
+35	C26     1.6230    -0.9550     9.5220	C.2	1	noname	-0.0320
+36	O5     4.3150     2.9760    12.3150	O.3	1	noname	-0.3514
+37	C27     5.5740     1.6220    15.1920	C.3	1	noname	-0.0030
+38	C28     5.9870     0.3810    14.5030	C.2	1	noname	-0.0541
+39	C29     6.4950     0.4310    13.2290	C.2	1	noname	-0.0567
+40	C30     6.5700    -0.7570    12.4530	C.2	1	noname	-0.0604
+41	C31     6.2220    -2.0410    12.9850	C.2	1	noname	-0.0616
+42	C32     5.7500    -2.0670    14.2970	C.2	1	noname	-0.0604
+43	C33     5.5910    -0.8980    15.0340	C.2	1	noname	-0.0567
+44	H1     3.5551     1.1833    15.8771	H	1	noname	0.0574
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diff --git a/prolif/data/vina/vina_output.sdf b/prolif/data/vina/vina_output.sdf
new file mode 100644
index 0000000..0d217b5
--- /dev/null
+++ b/prolif/data/vina/vina_output.sdf
@@ -0,0 +1,1547 @@
+
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+M  CHG  1  21   1
+M  END
+$$$$
+
+     RDKit          3D
+
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+M  CHG  1  21   1
+M  END
+$$$$
+
+     RDKit          3D
+
+ 79 86  0  0  0  0  0  0  0  0999 V2000
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+M  CHG  1  21   1
+M  END
+$$$$
+
+     RDKit          3D
+
+ 79 86  0  0  0  0  0  0  0  0999 V2000
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+M  CHG  1  21   1
+M  END
+$$$$
+
+     RDKit          3D
+
+ 79 86  0  0  0  0  0  0  0  0999 V2000
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+M  CHG  1  21   1
+M  END
+$$$$
+
+     RDKit          3D
+
+ 79 86  0  0  0  0  0  0  0  0999 V2000
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+M  CHG  1  21   1
+M  END
+$$$$
+
+     RDKit          3D
+
+ 79 86  0  0  0  0  0  0  0  0999 V2000
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+M  CHG  1  21   1
+M  END
+$$$$
+
+     RDKit          3D
+
+ 79 86  0  0  0  0  0  0  0  0999 V2000
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+    3.7862    1.0395   17.0315 H   0  0  0  0  0  0  0  0  0  0  0  0
+    5.2549    1.8105   16.3935 H   0  0  0  0  0  0  0  0  0  0  0  0
+    4.5494    1.8785   19.2784 H   0  0  0  0  0  0  0  0  0  0  0  0
+    5.5506   -0.2206   19.6966 H   0  0  0  0  0  0  0  0  0  0  0  0
+    4.6900   -0.5003   18.1449 H   0  0  0  0  0  0  0  0  0  0  0  0
+    6.4534   -0.1582   18.1449 H   0  0  0  0  0  0  0  0  0  0  0  0
+    7.1977    2.1737   18.4449 H   0  0  0  0  0  0  0  0  0  0  0  0
+    5.8579    2.6886   21.0306 H   0  0  0  0  0  0  0  0  0  0  0  0
+    7.1630    1.5020   20.5743 H   0  0  0  0  0  0  0  0  0  0  0  0
+    9.1321    3.0677   22.3520 H   0  0  0  0  0  0  0  0  0  0  0  0
+   10.2231    5.4462   21.6800 H   0  0  0  0  0  0  0  0  0  0  0  0
+    9.5190    7.5882   19.9202 H   0  0  0  0  0  0  0  0  0  0  0  0
+    8.0345    8.0671   17.9145 H   0  0  0  0  0  0  0  0  0  0  0  0
+    6.4579    6.4036   17.0645 H   0  0  0  0  0  0  0  0  0  0  0  0
+    4.6358    5.3273   17.3209 H   0  0  0  0  0  0  0  0  0  0  0  0
+    1.5038    0.2381   14.3407 H   0  0  0  0  0  0  0  0  0  0  0  0
+    2.4151   -1.4900   11.2140 H   0  0  0  0  0  0  0  0  0  0  0  0
+    3.4156   -1.3131    9.6578 H   0  0  0  0  0  0  0  0  0  0  0  0
+    5.2422   -2.5787    9.5940 H   0  0  0  0  0  0  0  0  0  0  0  0
+    6.8245   -4.0639   10.8461 H   0  0  0  0  0  0  0  0  0  0  0  0
+    7.1747   -3.8026   13.3511 H   0  0  0  0  0  0  0  0  0  0  0  0
+    5.7458   -2.1303   14.5619 H   0  0  0  0  0  0  0  0  0  0  0  0
+    3.9801   -0.8812   13.3737 H   0  0  0  0  0  0  0  0  0  0  0  0
+  1  2  1  0
+  1  4  1  0
+  1 11  1  0
+  1 16  1  1
+  2 12  2  0
+  2 13  1  0
+  3  5  1  0
+  3 13  1  0
+  3 37  1  0
+  4 10  1  0
+  5 14  2  0
+  5 15  1  0
+  6  7  1  0
+  6 15  1  0
+  7  8  1  0
+  8  9  1  0
+  9 10  1  0
+  9 15  1  0
+ 10 13  1  0
+ 10 36  1  1
+ 16 17  1  0
+ 17 18  2  0
+ 17 19  1  0
+ 19 20  1  0
+ 19 35  1  0
+ 20 21  1  0
+ 21 22  1  0
+ 21 23  1  0
+ 23 24  1  0
+ 23 25  1  0
+ 24 26  1  0
+ 25 34  1  0
+ 25 35  2  0
+ 26 27  2  0
+ 26 28  1  0
+ 27 29  1  0
+ 28 30  2  0
+ 28 34  1  0
+ 29 30  1  0
+ 30 31  1  0
+ 31 32  2  0
+ 32 33  1  0
+ 33 34  2  0
+ 37 38  1  0
+ 38 39  2  0
+ 38 43  1  0
+ 39 40  1  0
+ 40 41  2  0
+ 41 42  1  0
+ 42 43  2  0
+  3 44  1  6
+  6 45  1  0
+  6 46  1  0
+  7 47  1  0
+  7 48  1  0
+  8 49  1  0
+  8 50  1  0
+  9 51  1  6
+ 11 52  1  0
+ 11 53  1  0
+ 11 54  1  0
+ 16 55  1  0
+ 19 56  1  1
+ 20 57  1  0
+ 20 58  1  0
+ 21 59  1  1
+ 22 60  1  0
+ 22 61  1  0
+ 22 62  1  0
+ 23 63  1  6
+ 24 64  1  0
+ 24 65  1  0
+ 27 66  1  0
+ 29 67  1  0
+ 31 68  1  0
+ 32 69  1  0
+ 33 70  1  0
+ 35 71  1  0
+ 36 72  1  0
+ 37 73  1  0
+ 37 74  1  0
+ 39 75  1  0
+ 40 76  1  0
+ 41 77  1  0
+ 42 78  1  0
+ 43 79  1  0
+M  CHG  1  21   1
+M  END
+$$$$
+
+     RDKit          3D
+
+ 79 86  0  0  0  0  0  0  0  0999 V2000
+   -2.3500   -7.2670   13.4620 C   0  0  2  0  0  0  0  0  0  0  0  0
+   -2.3550   -6.4140   14.7830 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -3.8670   -6.1670   16.6630 C   0  0  1  0  0  0  0  0  0  0  0  0
+   -3.7760   -7.3940   13.2220 O   0  0  0  0  0  0  0  0  0  0  0  0
+   -4.8570   -7.2880   17.2240 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -6.6810   -8.9230   16.7470 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -6.9310   -9.6920   15.4740 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -6.3700   -8.8570   14.3350 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -5.1700   -8.1230   15.0890 C   0  0  1  0  0  0  0  0  0  0  0  0
+   -4.5410   -6.8930   14.3590 C   0  0  1  0  0  0  0  0  0  0  0  0
+   -1.6340   -8.4960   14.0530 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -1.3550   -5.9640   15.4010 O   0  0  0  0  0  0  0  0  0  0  0  0
+   -3.6650   -6.2760   15.2460 N   0  0  0  0  0  0  0  0  0  0  0  0
+   -4.8090   -7.4580   18.4400 O   0  0  0  0  0  0  0  0  0  0  0  0
+   -5.6740   -7.8870   16.3690 N   0  0  0  0  0  0  0  0  0  0  0  0
+   -1.8370   -6.7280   12.3210 N   0  0  0  0  0  0  0  0  0  0  0  0
+   -2.3740   -6.9520   11.0460 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -3.2920   -7.7330   10.8990 O   0  0  0  0  0  0  0  0  0  0  0  0
+   -1.7350   -6.1160    9.9620 C   0  0  1  0  0  0  0  0  0  0  0  0
+   -1.8770   -4.6410   10.4000 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -1.2030   -3.8180    9.3640 N   0  0  1  0  0  4  0  0  0  0  0  0
+   -1.7330   -2.3450    9.5240 C   0  0  0  0  0  0  0  0  0  0  0  0
+    0.2290   -4.0000    9.1880 C   0  0  2  0  0  0  0  0  0  0  0  0
+    0.8650   -3.3930    7.9040 C   0  0  0  0  0  0  0  0  0  0  0  0
+    0.5530   -5.5230    9.4750 C   0  0  0  0  0  0  0  0  0  0  0  0
+    2.3900   -3.6960    7.7820 C   0  0  0  0  0  0  0  0  0  0  0  0
+    3.4670   -3.1840    7.2180 C   0  0  0  0  0  0  0  0  0  0  0  0
+    2.7500   -4.8910    8.5250 C   0  0  0  0  0  0  0  0  0  0  0  0
+    4.5680   -4.0760    7.5490 N   0  0  0  0  0  0  0  0  0  0  0  0
+    4.1140   -5.1610    8.2790 C   0  0  0  0  0  0  0  0  0  0  0  0
+    4.7090   -6.2780    8.7430 C   0  0  0  0  0  0  0  0  0  0  0  0
+    3.9290   -7.1850    9.5620 C   0  0  0  0  0  0  0  0  0  0  0  0
+    2.6390   -6.9660    9.8590 C   0  0  0  0  0  0  0  0  0  0  0  0
+    2.0130   -5.8140    9.2540 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -0.2950   -6.4860    9.7880 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -5.3470   -5.8520   13.9220 O   0  0  0  0  0  0  0  0  0  0  0  0
+   -4.4520   -4.8040   17.0520 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -4.1710   -4.3060   18.4150 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -2.8980   -4.3590   18.9240 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -2.6920   -4.2040   20.3220 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -3.7610   -3.8940   21.2230 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -5.0380   -3.8020   20.6690 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -5.2610   -4.0390   19.3170 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -2.8686   -6.2959   17.1064 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -7.6083   -8.4513   17.1043 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -6.3276   -9.5704   17.5631 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -6.4221  -10.6663   15.5148 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -8.0057   -9.8790   15.3322 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -6.0080   -9.4860   13.5084 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -7.0953   -8.1831   13.8556 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -4.2686   -8.7528   15.1176 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -1.6639   -8.4447   15.1514 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -2.1394   -9.4126   13.7148 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -0.5873   -8.5084   13.7148 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -1.0080   -6.1218   12.3997 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -2.2252   -6.2849    8.9919 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -2.9402   -4.3671   10.4684 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -1.4242   -4.4781   11.3891 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -1.4692   -4.1701    8.4334 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -1.8208   -2.1010   10.5930 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -1.0292   -1.6481    9.0454 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -2.7195   -2.2562    9.0454 H   0  0  0  0  0  0  0  0  0  0  0  0
+    0.7517   -3.3713    9.9239 H   0  0  0  0  0  0  0  0  0  0  0  0
+    0.3522   -3.8127    7.0260 H   0  0  0  0  0  0  0  0  0  0  0  0
+    0.7486   -2.3007    7.9620 H   0  0  0  0  0  0  0  0  0  0  0  0
+    3.5219   -2.2647    6.6165 H   0  0  0  0  0  0  0  0  0  0  0  0
+    5.5495   -3.9214    7.2776 H   0  0  0  0  0  0  0  0  0  0  0  0
+    5.7608   -6.4960    8.5060 H   0  0  0  0  0  0  0  0  0  0  0  0
+    4.4147   -8.0899    9.9561 H   0  0  0  0  0  0  0  0  0  0  0  0
+    2.0832   -7.6342   10.5332 H   0  0  0  0  0  0  0  0  0  0  0  0
+    0.0452   -7.5242    9.9164 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -5.7188   -5.3719   14.7039 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -4.0586   -4.0616   16.3421 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -5.5422   -4.9483   17.0250 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -2.0410   -4.5206   18.2536 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -1.6761   -4.3264   20.7257 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -3.5842   -3.7364   22.2972 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -5.8871   -3.5362   21.3158 H   0  0  0  0  0  0  0  0  0  0  0  0
+   -6.2902   -4.0222   18.9291 H   0  0  0  0  0  0  0  0  0  0  0  0
+  1  2  1  0
+  1  4  1  0
+  1 11  1  0
+  1 16  1  6
+  2 12  2  0
+  2 13  1  0
+  3  5  1  0
+  3 13  1  0
+  3 37  1  0
+  4 10  1  0
+  5 14  2  0
+  5 15  1  0
+  6  7  1  0
+  6 15  1  0
+  7  8  1  0
+  8  9  1  0
+  9 10  1  0
+  9 15  1  0
+ 10 13  1  0
+ 10 36  1  6
+ 16 17  1  0
+ 17 18  2  0
+ 17 19  1  0
+ 19 20  1  0
+ 19 35  1  0
+ 20 21  1  0
+ 21 22  1  0
+ 21 23  1  0
+ 23 24  1  0
+ 23 25  1  0
+ 24 26  1  0
+ 25 34  1  0
+ 25 35  2  0
+ 26 27  2  0
+ 26 28  1  0
+ 27 29  1  0
+ 28 30  2  0
+ 28 34  1  0
+ 29 30  1  0
+ 30 31  1  0
+ 31 32  2  0
+ 32 33  1  0
+ 33 34  2  0
+ 37 38  1  0
+ 38 39  2  0
+ 38 43  1  0
+ 39 40  1  0
+ 40 41  2  0
+ 41 42  1  0
+ 42 43  2  0
+  3 44  1  1
+  6 45  1  0
+  6 46  1  0
+  7 47  1  0
+  7 48  1  0
+  8 49  1  0
+  8 50  1  0
+  9 51  1  1
+ 11 52  1  0
+ 11 53  1  0
+ 11 54  1  0
+ 16 55  1  0
+ 19 56  1  6
+ 20 57  1  0
+ 20 58  1  0
+ 21 59  1  6
+ 22 60  1  0
+ 22 61  1  0
+ 22 62  1  0
+ 23 63  1  1
+ 24 64  1  0
+ 24 65  1  0
+ 27 66  1  0
+ 29 67  1  0
+ 31 68  1  0
+ 32 69  1  0
+ 33 70  1  0
+ 35 71  1  0
+ 36 72  1  0
+ 37 73  1  0
+ 37 74  1  0
+ 39 75  1  0
+ 40 76  1  0
+ 41 77  1  0
+ 42 78  1  0
+ 43 79  1  0
+M  CHG  1  21   1
+M  END
diff --git a/prolif/fingerprint.py b/prolif/fingerprint.py
index a289f1c..fd85983 100644
--- a/prolif/fingerprint.py
+++ b/prolif/fingerprint.py
@@ -158,9 +158,12 @@ def list_available(show_hidden=False):
             underscore ``_``. Those are not supposed to be called directly)
         """
         if show_hidden:
-            return [name for name in _INTERACTIONS.keys()]
-        return [name for name in _INTERACTIONS.keys()
-                if not (name.startswith("_") or name == "Interaction")]
+            interactions = [name for name in _INTERACTIONS.keys()]
+        else:
+            interactions = [name for name in _INTERACTIONS.keys()
+                            if not (name.startswith("_")
+                                    or name == "Interaction")]
+        return sorted(interactions)
 
     @property
     def n_interactions(self):
@@ -290,6 +293,74 @@ def run(self, traj, lig, prot, residues=None, return_atoms=False,
         self.ifp = ifp
         return self
 
+    def run_from_iterable(self, lig_iterable, prot_mol, residues=None,
+            return_atoms=False, progress=True):
+        """Generates the fingerprint between a list of ligands and a protein
+
+        Parameters
+        ----------
+        lig_iterable : list or generator
+            An iterable yielding ligands as :class:`~prolif.molecule.Molecule`
+            objects
+        prot_mol : prolif.molecule.Molecule
+            The protein
+        residues : list or "all" or None
+            A list of protein residues (:class:`str`, :class:`int` or
+            :class:`~prolif.residue.ResidueId`) to take into account for
+            the fingerprint extraction.
+            If ``"all"``, all residues will be used.
+            If ``None``, at each frame the :func:`~prolif.utils.get_residues_near_ligand`
+            function is used to automatically use protein residues that are
+            distant of 6.0 Å or less from each ligand residue.
+        return_atoms : bool
+            For each residue pair and interaction, return indices of atoms
+            responsible for the interaction instead of bits.
+        progress : bool
+            Use the `tqdm <https://tqdm.github.io/>`_ package to display a
+            progressbar while running the calculation
+
+        Returns
+        -------
+        prolif.fingerprint.Fingerprint
+            The Fingerprint instance that generated the fingerprint
+        
+        Example
+        -------
+        ::
+
+            >>> prot = mda.Universe("protein.pdb")
+            >>> prot = plf.Molecule.from_mda(prot)
+            >>> lig_iter = plf.mol2_supplier("docking_output.mol2")
+            >>> fp = plf.Fingerprint()
+            >>> fp.run_from_iterable(lig_iter, prot)
+
+        """
+        iterator = tqdm(lig_iterable) if progress else lig_iterable
+        # set which residues to use
+        select_residues = False
+        if residues == "all":
+            resids = prot_mol.residues.keys()
+        elif isinstance(residues, Iterable):
+            resids = residues
+        else:
+            select_residues = True
+        ifp = []
+        for i, lig_mol in enumerate(iterator):
+            data = {"Frame": i}
+            lres = lig_mol[0]
+            if select_residues:
+                resids = get_residues_near_ligand(lres, prot_mol)
+            for prot_key in resids:
+                pres = prot_mol[prot_key]
+                if return_atoms:
+                    bv, value = self.bitvector_atoms(lres, pres)
+                else:
+                    value = self.bitvector(lres, pres)
+                data[(lres.resid, pres.resid)] = value
+            ifp.append(data)
+        self.ifp = ifp
+        return self
+
     def to_dataframe(self, **kwargs):
         """Converts fingerprints to a pandas DataFrame
 
diff --git a/prolif/molecule.py b/prolif/molecule.py
index 1bae60f..fe86cea 100644
--- a/prolif/molecule.py
+++ b/prolif/molecule.py
@@ -2,14 +2,17 @@
 Reading proteins and ligands --- :mod:`prolif.molecule`
 =======================================================
 """
+import copy
 from operator import attrgetter
-from MDAnalysis import _CONVERTERS
+import MDAnalysis as mda
+from rdkit import Chem
+from rdkit.Chem.AllChem import AssignBondOrdersFromTemplate
 from .rdkitmol import BaseRDKitMol
 from .residue import Residue, ResidueGroup
 from .utils import split_mol_by_residues
 
 
-mda_to_rdkit = _CONVERTERS["RDKIT"]().convert
+mda_to_rdkit = mda._CONVERTERS["RDKIT"]().convert
 
 
 class Molecule(BaseRDKitMol):
@@ -78,7 +81,7 @@ def __init__(self, mol):
     
     @classmethod
     def from_mda(cls, obj, selection=None, **kwargs):
-        """Create a Molecule from an MDAnalysis object
+        """Creates a Molecule from an MDAnalysis object
         
         Parameters
         ----------
@@ -111,6 +114,42 @@ def from_mda(cls, obj, selection=None, **kwargs):
         ag = obj.select_atoms(selection) if selection else obj.atoms
         mol = mda_to_rdkit(ag, **kwargs)
         return cls(mol)
+    
+    @classmethod
+    def from_rdkit(cls, mol, resname="UNL", resnumber=1, chain=""):
+        """Creates a Molecule from an RDKit molecule
+        
+        While directly instantiating a molecule with ``prolif.Molecule(mol)``
+        would also work, this method insures that every atom is linked to an
+        AtomPDBResidueInfo which is required by ProLIF
+        
+        Parameters
+        ----------
+        mol : rdkit.Chem.rdchem.Mol
+            The input RDKit molecule
+        resname : str
+            The default residue name that is used if none was found
+        resnumber : int
+            The default residue number that is used if none was found
+        chain : str
+            The default chain Id that is used if none was found
+        
+        Notes
+        -----
+        This method only checks for an existing AtomPDBResidueInfo in the first
+        atom. If none was found, it will patch all atoms with the one created
+        from the method's arguments (resname, resnumber, chain).
+        """
+        if mol.GetAtomWithIdx(0).GetMonomerInfo():
+            return cls(mol)
+        mol = copy.deepcopy(mol)
+        for atom in mol.GetAtoms():
+            mi = Chem.AtomPDBResidueInfo(f" {atom.GetSymbol():<3.3}",
+                                         residueName=resname,
+                                         residueNumber=resnumber,
+                                         chainId=chain)
+            atom.SetMonomerInfo(mi)
+        return cls(mol)
 
     def __iter__(self):
         for residue in self.residues.values():
@@ -127,3 +166,124 @@ def __repr__(self): # pragma: no cover
     @property
     def n_residues(self):
         return len(self.residues)
+
+
+def pdbqt_supplier(paths, template):
+    """Supplies molecules, given a path to PDBQT files
+
+    Parameters
+    ----------
+    paths : list
+        A list (or any iterable) of PDBQT files
+    template : rdkit.Chem.rdchem.Mol
+        A template molecule with the correct bond orders and charges. It must
+        match exactly the molecule inside the PDBQT file.
+
+    Returns
+    -------
+    suppl : generator
+        A generator object that will provide the :class:`Molecule` object as
+        you iterate over it.
+
+    Example
+    -------
+    The supplier is typically used like this::
+
+        >>> import glob
+        >>> pdbqts = glob.glob("docking/ligand1/*.pdbqt")
+        >>> lig_suppl = pdbqt_supplier(pdbqts, template)
+        >>> for lig in lig_suppl:
+        ...     # do something with each ligand
+
+    """
+    for pdbqt_path in paths:
+        pdbqt = mda.Universe(pdbqt_path)
+        # set attributes needed by the converter
+        elements = [mda.topology.guessers.guess_atom_element(x)
+                    for x in pdbqt.atoms.names]
+        pdbqt.add_TopologyAttr("elements", elements)
+        pdbqt.add_TopologyAttr("chainIDs", pdbqt.atoms.segids)
+        pdbqt.atoms.types = pdbqt.atoms.elements
+        # convert without infering bond orders and charges
+        mol = mda_to_rdkit(pdbqt.atoms, NoImplicit=False)
+        # assign BO from template then add hydrogens
+        mol = Chem.RemoveHs(mol, sanitize=False)
+        mol = AssignBondOrdersFromTemplate(template, mol)
+        mol = Chem.AddHs(mol, addCoords=True, addResidueInfo=True)
+        yield Molecule(mol)
+
+
+def sdf_supplier(path, **kwargs):
+    """Supplies molecules, given a path to an SDFile
+    
+    Parameters
+    ----------
+    path : str
+        A path to the .sdf file
+    resname : str
+        Residue name for every ligand
+    resnumber : int
+        Residue number for every ligand
+    chain : str
+        Chain ID for every ligand
+
+    Returns
+    -------
+    suppl : generator
+        A generator object that will provide the :class:`Molecule` object as
+        you iterate over it.
+
+    Example
+    -------
+    The supplier is typically used like this::
+
+        >>> lig_suppl = sdf_supplier("docking/output.sdf")
+        >>> for lig in lig_suppl:
+        ...     # do something with each ligand
+
+    """
+    suppl = Chem.SDMolSupplier(path, removeHs=False)
+    for mol in suppl:
+        yield Molecule.from_rdkit(mol, **kwargs)
+
+
+def mol2_supplier(path, **kwargs):
+    """Generates prolif.Molecule objects from a MOL2 file
+    
+    Parameters
+    ----------
+    path : str
+        A path to the .mol2 file
+    resname : str
+        Residue name for every ligand
+    resnumber : int
+        Residue number for every ligand
+    chain : str
+        Chain ID for every ligand
+
+    Returns
+    -------
+    suppl : generator
+        A generator object that will provide the :class:`Molecule` object as
+        you iterate over it.
+
+    Example
+    -------
+    The supplier is typically used like this::
+
+        >>> lig_suppl = mol2_supplier("docking/output.mol2")
+        >>> for lig in lig_suppl:
+        ...     # do something with each ligand
+
+    """
+    block = []
+    with open(path, "r") as f:
+        for line in f:
+            if block and line.startswith("@<TRIPOS>MOLECULE"):
+                mol = Chem.MolFromMol2Block("".join(block), removeHs=False)
+                yield Molecule.from_rdkit(mol, **kwargs)
+                block = []
+            block.append(line)
+        mol = Chem.MolFromMol2Block("".join(block), removeHs=False)
+        yield Molecule.from_rdkit(mol, **kwargs)
+                
diff --git a/tests/test_fingerprint.py b/tests/test_fingerprint.py
index 6d9661f..ab97954 100644
--- a/tests/test_fingerprint.py
+++ b/tests/test_fingerprint.py
@@ -6,6 +6,8 @@
                                 _return_first_element)
 from prolif.interactions import Interaction
 from prolif.residue import ResidueId
+from prolif.datafiles import datapath
+from prolif.molecule import sdf_supplier
 from .test_base import protein_mol, ligand_mol, u, ligand_ag, protein_ag
 
 
@@ -104,6 +106,12 @@ def test_run_return_atoms(self, fp_class):
         ifp.pop("Frame")
         atom_pair = list(ifp.values())[0]
         assert isinstance(atom_pair, list)
+    
+    def test_run_from_iterable(self, fp):
+        path = str(datapath / "vina" / "vina_output.sdf")
+        lig_suppl = list(sdf_supplier(path))
+        fp.run_from_iterable(lig_suppl[:2], protein_mol, progress=False)
+        assert len(fp.ifp) == 2
 
     def test_to_df(self, fp, fp_class):
         with pytest.raises(AttributeError, match="use the run method"):
diff --git a/tests/test_molecule.py b/tests/test_molecule.py
index acc01f4..1a7795d 100644
--- a/tests/test_molecule.py
+++ b/tests/test_molecule.py
@@ -1,6 +1,11 @@
 import pytest
-from prolif.molecule import Molecule
+from rdkit import Chem
+from prolif.molecule import (Molecule,
+                             pdbqt_supplier,
+                             mol2_supplier,
+                             sdf_supplier)
 from prolif.residue import ResidueId
+from prolif.datafiles import datapath
 from .test_base import TestBaseRDKitMol, rdkit_mol, ligand_rdkit, u
 
 
@@ -19,6 +24,17 @@ def test_from_mda(self):
         assert rdkit_mol[0].resid == mda_mol[0].resid
         assert (rdkit_mol.HasSubstructMatch(mda_mol) and
                 mda_mol.HasSubstructMatch(rdkit_mol))
+    
+    def test_from_rdkit(self):
+        rdkit_mol = Molecule(ligand_rdkit)
+        newmol = Molecule.from_rdkit(ligand_rdkit)
+        assert rdkit_mol[0].resid == newmol[0].resid
+        mol = Chem.MolFromSmiles("CCO")
+        newmol = Molecule.from_rdkit(mol)
+        assert newmol[0].resid == ResidueId("UNL", 1)
+        newmol = Molecule.from_rdkit(mol, "FOO", 42, "A")
+        assert newmol[0].resid == ResidueId("FOO", 42, "A")
+
 
     @pytest.mark.parametrize("key", [
         0,
@@ -36,3 +52,38 @@ def test_iter(self, mol):
 
     def test_n_residues(self, mol):
         assert mol.n_residues == mol.residues.n_residues
+
+
+class TestSupplier:
+    def test_pdbqt(self):
+        path = datapath / "vina"
+        pdbqts = sorted(path.glob("*.pdbqt"))
+        template = Chem.MolFromSmiles("C[NH+]1CC(C(=O)NC2(C)OC3(O)C4CCCN4C(=O)"
+                                      "C(Cc4ccccc4)N3C2=O)C=C2c3cccc4[nH]cc"
+                                      "(c34)CC21")
+        suppl = pdbqt_supplier(pdbqts, template)
+        mols = list(suppl)
+        assert isinstance(mols[0], Molecule)
+        assert len(mols) == len(pdbqts)
+
+    def test_sdf(self):
+        path = str(datapath / "vina" / "vina_output.sdf")
+        suppl = sdf_supplier(path)
+        mols = list(suppl)
+        assert isinstance(mols[0], Molecule)
+        assert len(mols) == 9
+        mi = mols[0].GetAtomWithIdx(0).GetMonomerInfo()
+        assert all([mi.GetResidueName() == "UNL",
+                    mi.GetResidueNumber() == 1,
+                    mi.GetChainId() == ""])
+
+    def test_mol2(self):
+        path = str(datapath / "vina" / "vina_output.mol2")
+        suppl = mol2_supplier(path)
+        mols = list(suppl)
+        assert isinstance(mols[0], Molecule)
+        assert len(mols) == 9
+        mi = mols[0].GetAtomWithIdx(0).GetMonomerInfo()
+        assert all([mi.GetResidueName() == "UNL",
+                    mi.GetResidueNumber() == 1,
+                    mi.GetChainId() == ""])