✳flâneur — a map of the web's best reading

A Step Towards Generalisability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening | bioRxiv

biorxiv.org · 1,293 words · saved by 1 readers

Over the last few years, many 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. Only a scoring function that accounts for the interatomic interactions involved in binding can accurately predict binding affinity on unseen molecules. However, many scoring functions make predictions based on dataset biases rather than an understanding of the physics of binding. These scoring functions perform well when tested on similar targets to those in the training set, but fail to generalise to dissimilar targets. To test what a machine learning-based scoring function has learnt, input attribution—a technique for learning which features are important to a model when making a prediction on a particular data point—can be applied. If a model successfully learns something beyond dataset biases, attribution should give insight into the important binding interactions that are taking place. We built a machine learning-based scoring function that aimed to avoid the influence of bias via thorough train and test dataset filtering, and show that it achieves comparable performance on the CASF-2016 benchmark to other leading methods. We then use the CASF-2016 test set to perform attribution, and find that the bonds identified as important by PointVS, unlike those extracted from other scoring functions, have a high correlation with those found by a distance-based interaction profiler. We then show that attribution 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 the scoring function has learnt to identify some important binding interactions, but also constitutes the first deep learning-based method for extracting structural information from a target for molecule design. ### Competing Interest Statement The authors have declared no competing interest.

A Step Towards Generalisability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening | bioRxiv Skip to main content New Results A Step Towards Generalisability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening View ORCID Profile Jack Scantlebury , View ORCID Profile Lucy Vost , View ORCID Profile Anna Carbery , View ORCID Profile Thomas E. Hadfield , View ORCID Profile Oliver M. Turnbull , View ORCID Profile Nathan Brown , View ORCID Profile Vijil Chenthamarakshan , Payel Das , View ORCID Profile Harold Grosjean , View ORCID Prof

Explore this link on the map →

related reading