The Playbooks of Medicinal Chemistry Design Moves

Awale, Mahendra; Hert, Jérôme; Guasch, Laura; Riniker, Sereina; Krämer, Christian

doi:10.1021/acs.jcim.0c01143

Cited by 17 publications

(18 citation statements)

References 42 publications

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“…One can apply R-group substitutions to attach functional groups at specific positions on a structure or utilize techniques such as positional analog scanning to generate sets of closely related molecules. Several alternate approaches , analyze databases of bioactive molecules to identify common medicinal chemistry transformations. These transformations can then be programmatically applied to chemical structures to generate plausible analogs.…”

Section: Acd Levelmentioning

confidence: 99%

Defining Levels of Automated Chemical Design

et al. 2022

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One application area of computational methods in drug discovery is the automated design of small molecules. Despite the large number of publications describing methods and their application in both retrospective and prospective studies, there is a lack of agreement on terminology and key attributes to distinguish these various systems. We introduce Automated Chemical Design (ACD) Levels to clearly define the level of autonomy along the axes of ideation and decision making. To fully illustrate this framework, we provide literature exemplars and place some notable methods and applications into the levels. The ACD framework provides a common language for describing automated small molecule design systems and enables medicinal chemists to better understand and evaluate such systems.

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Section: Acd Levelmentioning

confidence: 99%

Defining Levels of Automated Chemical Design

et al. 2022

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“…In this work, we show that transformer models are not only able to generate new molecular structures absent from the training dataset, but, in doing so, also go beyond the standard matched molecular pairs (MMP)-based approach. 3,4,16 To demonstrate this, we first generated all MMP transformation rules (in the form of SMIRKS) for our training subset of ChEMBL 15 molecules; expectedly, 4,16 the most ubiquitous of them were additions or replacements of single atoms (H, F, Cl, etc.) or simple groups (methyl, methoxy, ethyl, etc.)…”

Section: Transformer Models Trained On Pairs Of Bioactive Molecules C...mentioning

confidence: 99%

“…However, applications of ML specifically to hit expansion have limited demonstrated success. 2,[3][4][5][6][7][8][9] A question arises whether this practice could be improved with ML, and if so, what kind of ML models could better serve this purpose.…”

Section: Introductionmentioning

confidence: 99%

Can We Quickly Learn to “Translate” Bioactive Molecules with Transformer Models?

Tysinger

Brajesh

Sinitskiy

2022

Preprint

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Meaningful exploration of the chemical space of druglike molecules in drug design is a highly challenging task due to a combinatorial explosion of possible modifications of molecules. In this work, we address this problem with transformer models, a type of machine learning (ML) model, with recent demonstrated success in applications to machine translation and other tasks. By training transformer models on pairs of similar bioactive molecules from the public ChEMBL dataset, we enable them to learn medicinal-chemistry-meaningful, context-dependent transformations of molecules, including those absent from the training set. Most generated molecules are highly plausible and follow similar distributions of simple properties (molecular weight, polarity, hydrogen bond donor and acceptor numbers) as the training dataset. By retrospective analysis of the performance of transformer models on ChEMBL subsets of ligands binding to COX2, DRD2, or HERG protein targets, we demonstrate that the models can generate structures identical or highly similar to highly active ligands, despite the models having not seen any ligands active against the corresponding protein target during training. Thus, our work demonstrates that transformer models, originally developed to translate texts from one natural language to another, can be easily and quickly extended to “translations” from known molecules active against a given protein target to novel molecules active against the same target, and thereby contribute to hit expansion in drug design.

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“…In parallel, rule-based molecular generation is also very popular such as AbbVies project Drug Guru [30,31], the abbreviation of drug generation using rules. A data-driven method called matched molecular pairs (MMPs) [59][60][61] is another way to collect the experts knowledge from literature. Indeed, the rules of Drug Guru and MMPs are essentially the same method and nearly from the same source, that is molecular design thoughts of human beings.…”

Section: Case: 3-phosphoglycerate Dehydrogenase (Phgdh)mentioning

confidence: 99%

Systemic Evolutionary Chemical Space Exploration For Drug Discovery

Lu¹,

Liu²,

Shi³

et al. 2021

Preprint

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Chemical space exploration is a major task of the hit-finding process during the pursuit of novel chemical entities. Compared with other screening technologies, computational de novo design has become a popular approach to overcome the limitation of current chemical libraries. Here, we reported a de novo design platform named systemic evolutionary chemical space explorer (SECSE). The platform was conceptually inspired by fragment-based drug design, that miniaturized a “lego-building” process within the pocket of a certain target. The key of virtual hits generation was then turned into a computational search problem. To enhance search and optimization, human intelligence and deep learning were integrated. Application of SECSE against PHGDH, proved its potential in finding novel and diverse small molecules that are attractive starting points for further validation. This platform is open-sourced and the code is available at http://github.com/KeenThera/SECSE.

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The Playbooks of Medicinal Chemistry Design Moves

Cited by 17 publications

References 42 publications

Defining Levels of Automated Chemical Design

Defining Levels of Automated Chemical Design

Can We Quickly Learn to “Translate” Bioactive Molecules with Transformer Models?

Systemic Evolutionary Chemical Space Exploration For Drug Discovery

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