Predicting Strategies for Lead Optimization via Learning to Rank

MolSHAP: Interpreting Quantitative Structure-Activity Relationships Using Shapley Values of R-Groups

Optimizing the activities and properties of lead compounds is an essential step in the drug discovery process. Despite recent advances in machine learning-aided drug discovery, most of the existing methods focus on making predictions for the desired objectives directly while ignoring the explanations for predictions. Although several techniques can provide interpretations for machine learning-based methods such as feature attribution, there are still gaps between these interpretations and the principles commonly adopted by medicinal chemists when designing and optimizing molecules. Here, we propose an interpretation framework, named MolSHAP, for quantitative structure-activity relationship analysis by estimating the contributions of R-groups. Instead of attributing the activities to individual input features, MolSHAP regards the R-group fragments as the basic units of interpretation, which is in accordance with the fragment-based modifications in molecule optimization. MolSHAP is a model-agnostic method that can interpret activity regression models with arbitrary input formats and model architectures. Based on the evaluations of numerous representative activity regression models on a specially designed R-group ranking task, MolSHAP achieved significantly better interpretation power compared with other methods. In addition, we developed a compound optimization algorithm based on MolSHAP and illustrated the reliability of the optimized compounds using an independent case study. These results demonstrated that MolSHAP can provide a useful tool for accurately interpreting the quantitative structure-activity relationships and rationally optimizing the compound activities in drug discovery.

Computer aided drug discovery review for infectious diseases with case study of anti-Chagas project

Neglected tropical diseases (NTDs) are parasitic and bacterial infections that are widespread, especially in the tropics, and cause health problems for about one billion people over 149 countries worldwide. However, in terms of therapeutic agents, for example, nifurtimox and benznidazole were developed in the 1960s to treat Chagas disease, but new drugs are desirable because of their side effects. Drug discovery takes 12 to 14 years and costs $2.6 billon dollars, and hence, computer aided drug discovery (CADD) technology is expected to reduce the time and cost. This paper describes our methods and results based on CADD, mainly for NTDs. An overview of databases, molecular simulation and pharmacophore modeling, contest-based drug discovery, and machine learning and their results are presented herein.

libmolgrid: Graphics Processing Unit Accelerated Molecular Gridding for Deep Learning Applications

We describe libmolgrid, a general-purpose library for representing three-dimensional molecules using multidimensional arrays of voxelized molecular data. libmolgrid provides functionality for sampling batches of data suited to machine learning workflows, and it also supports temporal and spatial recurrences over that data to facilitate work with convolutional and recurrent neural networks. It was designed for seamless integration with popular deep learning frameworks and features optimized performance by leveraging graphics processing units (GPUs). libmolgrid is a free and open source project (GPLv2) that aims to democratize grid-based modeling in computational chemistry.