The concept of in silico molecular design goes back decades and has a long history of published approaches using many different algorithms and models [1,2]. Major challenges involved in de novo molecular design are manifold, including identifying appropriate molecular representations for optimisation, scoring designed molecules against multiple modelled endpoints, and objectively quantifying synthetic feasibility of the designed structures.
Recently, multiobjective de novo design, more recently referred to as generative chemistry, has had a resurgence of interest. This renaissance has highlighted a step-change in successful applications of such methods. This presentation will review the development of de novo design methods over the years including the author’s original work in this area from the early 2000s , to recent approaches that show great promise [4,5]. Through this review, improvements in important components of de novo design, including machine learning model predictions and automated synthesis planning, will also be presented.
 Nicolaou, C. A., Brown, N., Pattichis, C. S. Molecular optimization using computational multi-objective methods. Current Opinion in Drug Discovery and Development, 2007, 10(3), 316-324.
 Nicolaou, C .A., Brown, N. Multi-objective optimization methods in drug design. Drug Discov. Today: Technol. 2013, 10(3), e427-e435.
 Brown, N.; McKay, B.; Gilardoni, F. A Graph-Based Genetic Algorithm and Its Application to the Multiobjective Evolution of Median Molecules. J. Chem. Inf. Comput. Sci. 2004, 44(3), 1079-1087.
 Neil, D.; Segler, M.; Guasch, L.; Ahmed, M.; Plumbley, D.; Sellwood, M.; Brown, N. Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design. 2018. https://openreview.net/forum?id=HkcTe-bR-
 Brown, N.; Fiscato, M.; Segler, M. H. S. Vaucher, A. C. GuacaMol: Benchmarking Models for De Novo Molecular Design. J. Chem. Inf. Model. 2019. 53(3), 1096-1108.
Nathan is recognised as a global thought-leader in Chemoinformatics and computational drug discovery, and is the inventor of the first multiobjective de novo molecular design system. He joined BenevolentAI in 2017 from The Institute of Cancer Research, London where he founded and led the In Silico Medicinal Chemistry team for over ten years, with significant scientific impact on drugs in active clinical trials, and the development of new algorithms for drug discovery. Nathan has published over 40 papers and three books; is a Fellow of The Royal Society of Chemistry; and is the 2017 recipient of the Corwin Hansch Award.