Over the last few years, many new machine learning-based scoring functions for predicting the binding of small molecules to proteins have been developed. Their objective is to approximate the distribution which takes two molecules as input and outputs the energy of their interaction. This distribution is dependent on the interatomic interactions involved in binding, and only a scoring function that accounts for these interactions can accurately predict binding affinity on unseen molecules. To try to create a method capable of learning these interactions, we built PointVS: a machine learning-based scoring function which achieves state of the art performance even after rigorous filtering of the training set. This filtering is key, as we found that a commonly used benchmark, CASF-16, overestimates the true accuracy of machine learning-based scoring functions when trained using the most commonly used training set. Ranking algorithms using this benchmark rewards memorisation of training data rather than knowledge of the rules of intermolecular binding. We demonstrate that PointVS is able to identify important interactions using attribution, and further, that it can be used to extract important binding pharmacophores from a given protein target when supplied with a number of bound structures. We use this information to perform fragment elaboration, and see improvements in docking scores compared to using structural information from a traditional data-based approach. This not only provides definitive proof that PointVS is learning to identify important binding interactions, but also constitutes the first deep learning-based method for extracting structural information from a target for molecule design.

Antibodies with lambda light chains (lambda-antibodies) are generally considered to be less developable than those with kappa light chains (kappa-antibodies), leading to substantial systematic biases in drug discovery pipelines. This has contributed to kappa dominance amongst clinical-stage therapeutics. However, the identification of increasing numbers of epitopes preferentially engaged by lambda-dash antibodies shows there is a functional cost to neglecting them as potential lead candidates during discovery campaigns. Here, we update our Therapeutic Antibody Profiler (TAP) tool to use the latest data and machine learning-based structure prediction methods, and apply this new protocol to evaluate developability risk profiles for kappa-dash antibodies and lambda-dash antibodies based on their surface physicochemical properties. We find that lambda-dash antibodies are on average at a higher risk of poor developability - as an indication, over 40% of single-cell sequenced human lambda-antibodies are flagged by TAP for risk-prone patches of surface hydrophobicity (PSH), compared to around 11% of human kappa-antibodies. Nonetheless, a substantial proportion of natural lambda-antibodies are assigned more moderate risk profiles by TAP and should therefore represent more tractable candidates for therapeutic development. We also analyse the populations of high and low risk antibodies, highlighting opportunities for strategic design that TAP suggests would enrich for more developable lambda-based candidates. Overall, we provide context to the differing developability of kappa- and lambda-antibodies, enabling a rational approach to incorporate more diversity into the initial pool of immunotherapeutic candidates.