Fingerprint

Introducció

Cheminformatics datasets contain molecules represented as SMILES.

These SMILES can be analysed using the RDKit library to get information about the atoms and bonds in the molecules.

Molecular fingerprinting is a vectorized representation of molecules capturing precise details of atomic configurations. During the featurization process, a molecule is decomposed into substructures (e.g., fragments) of a fixed-length binary fingerprint assembled into an array whose each element is either 1 or 0.

Deep learning models almost always take arrays of numbers as their inputs. If we want to process molecules with them, we somehow need to represent each molecule as one or more arrays of numbers.

Many (but not all) types of models require their inputs to have a fixed size. This can be a challenge for molecules, since different molecules have different numbers of atoms. If we want to use these types of models, we somehow need to represent variable sized molecules with fixed sized arrays.

Fingerprints are designed to address these problems. A fingerprint is a fixed length array, where different elements indicate the presence of different features in the molecule. If two molecules have similar fingerprints, that indicates they contain many of the same features, and therefore will likely have similar chemistry.

Deepchem

DeepChem supports a particular type of fingerprint called an “Extended Connectivity Fingerprint”, or “ECFP” for short. They also are sometimes called “circular fingerprints”. The ECFP algorithm begins by classifying atoms based only on their direct properties and bonds. Each unique pattern is a feature. For example, “carbon atom bonded to two hydrogens and two heavy atoms” would be a feature, and a particular element of the fingerprint is set to 1 for any molecule that contains that feature. It then iteratively identifies new features by looking at larger circular neighborhoods. One specific feature bonded to two other specific features becomes a higher level feature, and the corresponding element is set for any molecule that contains it. This continues for a fixed number of iterations, most often two.

Let’s take a look at a dataset that has been featurized with ECFP.

asks, datasets, transformers = dc.molnet.load_tox21(featurizer='ECFP')
train_dataset, valid_dataset, test_dataset = datasets
print(train_dataset)

TODO

• Molecular Deep Learning using DeepChem