De novo Drug Design – Ye olde Scoring Problem Revisited Gisbert Schneider, [a] Kimito Funatsu, [b] Yasushi Okuno, [c] and Dave Winkler [d] Generating molecules by computational means is common practice in drug discovery. New chemical entities with the desired properties may serve both as tool compounds for chemogenomics studies and as starting points for hit-to- lead expansion. The three challenges for automated de novo design are i) the assembly of synthetically accessible structures, ii) scoring and property prediction, and finally iii) the systematic optimization of promising molecules in adaptive learning cycles. [1] During the past three decades, numerous methods, algorithms and heuristics have been proposed to address each of these problems. [2] While the generation of new chemical entities with attractive chemi- cal scaffolds has become feasible by reaction-driven frag- ment assembly, and the in silico optimization problem may also be considered largely solved, the persisting issue of compound scoring remains difficult. Scoring means picking the best compounds from a large pool of accessible possi- bilities. [3] This process typically includes both ligand- and structure(receptor)-based virtual screening of the computa- tionally generated molecules. This large virtual compound pool contains many more inactive or problematic chemical structures than desirable ones. While compound elimina- tion by appropriate scoring models discards the bulk of the designs (“negative design”) with acceptable accuracy, the selection of the best or most promising ones (“positive design”) remains error-prone. The conventional techniques employed at this step of the selection process include coarse-grained and application-specific heuristics, [4,5] physi- cochemical property calculation, [6] quantitative and qualita- tive structure-activity relationship models, [7] similarity calcu- lations on various levels of detail and with different molec- ular representations, [8] shape matching and automated ligand docking, as well as the detection of potentially toxic and otherwise unwanted chemical structures. [9] More re- cently, qualitative and quantitative on- and off-target pre- diction methods have been added to the molecular design- er’s tool chest. [10] During the PacifiChem2015 conference in Honolulu, HI, USA, we organized a symposium to share experience and discuss the progress in computer-based de novo com- pound design and scoring (Figure 1). Several symposium papers and closely related contributions are compiled in this focused special issue of Molecular Informatics. It is evi- dent that project-specific, customized scoring functions will help reduce the false-positive prediction rate. In this special issue, we highlight methods that diverge from mainstream approaches and point towards future developments in mo- lecular informatics research. In a Methods Corner article, Kaneko and Funatsu review a structure generation method that can be used to design molecules that lie within the applicability domain of a scoring function. This “inverse QSAR” approach to de novo design is now awaiting practi- cal application. Fukunishi et al. present methods to improve docking scores by regression-based correction for use in structure-based virtual screening and molecule design. In a cross-validation study, the weighted scoring functions showed improved accuracy over simpler methods. Multidi- mensional compound optimization by Pareto ranking is showcased in the article by Daeyaert and Deem. They show that Pareto sorting improves the performance of de novo design algorithms in generating molecules with hard-to- meet constraints. This tackles the important problem of Figure 1. Researchers from across the globe discussed the status and future possibilities of de novo drug design in a special sympo- sium held at PacifiChem2015 in Honolulu, Hawaii. [a] G. Schneider Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH- 8093 Zurich, Switzerland [b] K. Funatsu Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan [c] Y. Okuno Graduate School of Medicine, Kyoto University, Shogoin- kawaharacho, Sakyo-ku Kyoto 606-8507, Japan [d] D. Winkler CSIRO Manufacturing, Bayview Ave, Clayton Vic. 3168, Australia  2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim Mol. Inf. 2017, 36, 1681031 (1 of 2) 1681031 Editorial www.molinf.com