Enabling future drug discovery by de novo design

Abstract

Computer‐assisted drug design is evolving as a source of innovation for drug discovery. In particular, de novo design approaches are being increasingly applied to find novel drug‐like compounds, molecular scaffolds, and bioisosteric replacements for established or unwanted fragments. Although some of the early software tools had a certain tendency to generate compounds of limited chemical attraction, modern de novo design algorithms put a strong emphasis on the synthesizability and drug‐likeness of machine‐generated compounds. We give an overview of the various methodologies for virtual compound construction, evaluation, and optimization in machina, and how they can support medicinal chemistry projects in the early phase of drug discovery. © 2011 John Wiley & Sons, Ltd. WIREs Comput Mol Sci 2011 1 742–759 DOI: 10.1002/wcms.49 This article is categorized under: Computer and Information Science > Chemoinformatics

GuacaMol: Benchmarking Models for de Novo Molecular Design

“…Molecules can be constructed atom-by-atom or by combination of fragments. 13 They can be grown in the context of a binding pocket, [20][21][22][23][24] which allows for structure-based scoring, or be constructed and scored using entirely ligand-based methods. Choosing a very general representation of molecules for construction, such as atom-by-atom and bond-bybond building, allows for potentially exploring all of chemical space.…”

“…amide couplings), or fragments derived from known molecules. 13,26,27 This, however, may drastically limit the potentially explorable space. This compromise between realistic molecules and large explorable molecular space is a major dilemma of de novo design.…”

Designing the molecular future

“…After a first wave of software tools and groundbreaking applications in the 1990s [14-18]-expressly GROW [19], GrowMol [20], LEGEND [21, 22], and LUDI [23,24] representing some of the key playerswe are currently witnessing a renewed strong interest in this field. Innovative ideas for both receptor-and ligandbased drug design have recently been published [25,26]. We here provide a personal perspective on the evolution of de novo design, highlighting some of the historic achievements as well as possible future developments of this exciting field of research, which combines multiple scientific disciplines and is, like few other areas in chemistry, subject to continuous enthusiastic discussion and compassionate dispute.…”

“…This was one reason for choosing peptides and peptide-mimetics as a preferred molecule class for exploration. Among the various algorithms that have been employed for de novo design ever since, the methods for navigation in chemical space probably have diversified the most, ranging from exhaustive product enumeration and deterministic combinatorial approaches to stochastic sampling by evolutionary algorithms and particle swarms, simulated annealing, and Markov chains, to name just some of the prominent examples [26]. By method transfer the field has massively benefited from parallel developments in computer science and engineering.…”

Wirkstoffe nach Zahlen: reaktionsbasierter De‐novo‐Entwurf von potenten und selektiven Leitstrukturen für die Krebsforschung

“…[1][2][3][4] Rechnerbasiertes "De-novo"-Design bietet sich als eine Methode der Wahl zu genau diesem Zweck an, da mittels dieses Ansatzes innovative molekulare Grundgerüste ("Chemotypen") aus einem scheinbar unendlichen chemischen Raum zugänglich gemacht werden kçnnen. [5][6][7] Hier berichten wir über die erfolgreiche Anwendung eines vollautomatischen und chemiegetriebenen De-novo-Designverfahrens, mit dessen Hilfe ein neuartiger, niedermolekularer, potenter und selektiver Inhibitor der humanen Polo-like Kinase 1 (hPlk1) entdeckt wurde. Diese potentielle Antitumor-Substanz wurde vom Designalgorithmus von Grund auf neu entworfen und konnte entsprechend dem vom Computerprogramm vorgeschlagenen Syntheseweg chemisch dargestellt werden.…”