Learning to Discover Medicines

TL;DR

This paper reviews recent AI advances in drug discovery, focusing on representation learning, data-driven molecular reasoning, and knowledge-based reasoning, highlighting challenges and future research directions.

Contribution

It provides a comprehensive organization of AI methods in drug discovery into three key sub-areas, summarizing recent progress and identifying open challenges.

Findings

AI accelerates drug discovery processes

Representation learning improves molecular understanding

Knowledge graphs enable reasoning over biomedical data

Abstract

Discovering new medicines is the hallmark of human endeavor to live a better and longer life. Yet the pace of discovery has slowed down as we need to venture into more wildly unexplored biomedical space to find one that matches today's high standard. Modern AI-enabled by powerful computing, large biomedical databases, and breakthroughs in deep learning-offers a new hope to break this loop as AI is rapidly maturing, ready to make a huge impact in the area. In this paper we review recent advances in AI methodologies that aim to crack this challenge. We organize the vast and rapidly growing literature of AI for drug discovery into three relatively stable sub-areas: (a) representation learning over molecular sequences and geometric graphs; (b) data-driven reasoning where we predict molecular properties and their binding, optimize existing compounds, generate de novo molecules, and plan the synthesis of target molecules; and (c) knowledge-based reasoning where we discuss the construction and reasoning over biomedical knowledge graphs. We will also identify open challenges and chart possible research directions for the years to come.