Oncological drug discovery: AI meets structure-based computational research

Drug Discovery Today, vol. 27, pp. 1661–1670

Abstract

The integration of machine learning and structure-based methods has proven valuable in the past as a way to prioritize targets and compounds in early drug discovery. In oncological research, these methods can be highly beneficial in addressing the diversity of neoplastic diseases portrayed by the different hallmarks of cancer. Here, we review six use case scenarios for integrated computational methods, namely driver prediction, computational mutagenesis, (off)-target prediction, binding site prediction, virtual screening, and allosteric modulation analysis. We address the heterogeneity of integration approaches and individual methods, while acknowledging their current limitations and highlighting their potential to bring drugs for personalized oncological therapies to the market faster.

Authors 5

1. Marina Gorostiola González Aachen Division of Drug Discovery and Safety Leiden Academic Centre for Drug Research

   Leiden University · Oncode Institute

   Affiliation as printed

   Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, the Netherlands; Oncode Institute, Utrecht, the Netherlands

   Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, the Netherlands

   Oncode Institute, Utrecht, the Netherlands

2. Antonius P. A. Janssen Aachen Molecular Physiology

   Leiden University · Oncode Institute

   Affiliation as printed

   Oncode Institute, Utrecht, the Netherlands; Molecular Physiology, Leiden Institute of Chemistry, Leiden University, the Netherlands

   Molecular Physiology, Leiden Institute of Chemistry, Leiden University, the Netherlands

   Oncode Institute, Utrecht, the Netherlands

3. Adriaan P. IJzerman Aachen Division of Drug Discovery and Safety Leiden Academic Centre for Drug Research

   Leiden University

   Affiliation as printed

   Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, the Netherlands

4. Laura H. Heitman Aachen Division of Drug Discovery and Safety Leiden Academic Centre for Drug Research

   Leiden University · Oncode Institute

   Affiliation as printed

   Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, the Netherlands; Oncode Institute, Utrecht, the Netherlands

   Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, the Netherlands

   Oncode Institute, Utrecht, the Netherlands

5. Gerard J. P. van Westen corresponding Aachen Division of Drug Discovery and Safety Leiden Academic Centre for Drug Research

   Leiden University

   Affiliation as printed

   Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, the Netherlands. Electronic address: gerard@lacdr.leidenuniv.nl

   Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, the Netherlands