Machine learning for the prediction of safe and biologically active organophosphorus molecules

TL;DR

This paper presents a machine learning framework using RNNs with attention to generate organophosphorus molecules with potential biological activity and reduced toxicity, aiding drug discovery.

Contribution

It introduces a novel RNN-based approach with attention for fragment-based chemical space sampling of organophosphorus molecules.

Findings

Successfully generated molecules with desired biological activity

Framework trained on high druglikeness ZINC dataset

Generated molecules contain specific starting fragment PO2F

Abstract

Drug discovery is a complex process with a large molecular space to be considered. By constraining the search space, the fragment-based drug design is an approach that can effectively sample the chemical space of interest. Here we propose a framework of Recurrent Neural Networks (RNN) with an attention model to sample the chemical space of organophosphorus molecules using the fragment-based approach. The framework is trained with a ZINC dataset that is screened for high druglikeness scores. The goal is to predict molecules with similar biological action modes as organophosphorus pesticides or chemical warfare agents yet less toxic to humans. The generated molecules contain a starting fragment of PO2F but have a bulky hydrocarbon side chain limiting its binding effectiveness to the targeted protein.