A general workflow for developing a 3D-QSAR model involves uploading a dataset with Py-MolEdit, optionally aligning it with Py-Align and then build a model with Py-CoMFA. During the years we extended our toolkit to include Comformational analysis (Py-ConfSearch), docking (Py-Docking), a different QSAR algorithm (Py-ComBinE) and an interface to the Protein Data Bank (Py-PDB).
Our work is heavily focused on modern state-of-the-art research that although extremely promising, also constitutes uncharted territory for actual usage in drug-discovery. We want to bridge that gap.
GCNs are a special kind of Neural Network which takes a graph as input: Each node can have its own properties and the learning algorithm synthesizes this information into features that are used for predicting some kind of value. It is immediate to realize how suited this technology can be for dealing with molecules. We are working on these kind of tools to make an important leap into the development of QSAR models that take into account more molecular information and doing so in an intuitive yet effective manner.
A common limit of neural networks especially in delicate fields like medicine and drug-design, is their "opaqueness": we as humans cannot understand the reasons for why a network gives a specific output or why it recognizes a molecule to be active or not. Modern research focuses on getting this insight and understand which parts of the input are influencing the output the most. Applying Explainability to molecules we are able to understand which parts are contributing the most and let researchers focus on those building a map around the molecule. We are able to do so with highly effective architectures that previously may have been discarded because of unexplainable predictions.