Machine learning (ML) algorithms, and more recently deep learning (DL) methods, have proven to perform well in different chemical related fields, and are thus broadly used in drug design and toxicity prediction. Given a labeled data set with known outcome, the ML algorithm learns to identify the often highly non-linear combinations of physico-chemical and structural features in the underlying data (e.g. compounds, protein structures or complexes) that may be responsible for their (toxic) effect.
Determining the toxicity of compounds is vital to identify their harmful effects on humans, animals, plants and the environment. We focus on several aspects of machine learning (ML) in the context of toxicity prediction, e.g. investigating into novel descriptors, applicability of models to external data and interpretability of deep learning (DL) models (toxicophore). These investigations aim to come closer to the vision of transforming toxicology into a predictive science and reducing the number of animal testing.
DeeplearningVS is a project which aims to study a novel rescoring method based on deep learning (DL) techniques to enhance the accuracy of docking results, and boost the structure-based virtual screening outcome.