Wednesday 07 December 2022 – Wednesday 07 December 2022

Time: 10 am PT/1 pm ET/6 pm BT


Fuelled by the success of machine learning in a wide range of domains, there is significant interest in the application of machine learning to early stage drug discovery in areas from designing novel compounds to screening libraries of compounds against a specific target.

There has been particular interest in machine-learning based scoring functions for predicting the binding of small molecules to target proteins. The aim of these functions is to approximate the distribution which takes two molecules as input and outputs the energy of their interaction. This distribution is dependent on interactions between the atoms of the two molecules and the solvent, and only a scoring function that accounts for these interactions can accurately predict binding affinity on novel/unseen molecules.

To attempt to create a method capable of learning these interactions we built PointVS, a machine learning scoring function, which achieves state-of-the-art performance even after performing rigorous filtering of the training set. PointVS is able to identify important interactions.  PointVS  appears able to identify important binding interactions, and is the first deep learning-based method for extracting important binding information from a target for molecule design.


About Speaker: Charlotte Deane, a professor of Structural Bioinformatics and former Head of the Department of Statistics at the University of Oxford. She completed her undergraduate education at University College, Oxford, and went to the University of Cambridge to study structural bioinformatics. Prof. Deane worked as a Wellcome Trust Research Fellow for two years. She was recently awarded an MBE in the Queen’s Birthday Honours. Her research focuses on protein structure prediction, particularly antibodies. Her research group, Oxford Protein Informatics Group (OPIG), created the SABDab, a database for antibody structures, and the SAbPred, a webserver for antibody structure prediction. In addition, Prof. Deane's research also focuses on immuninformatics, biological networks, and small molecule drug discovery.


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