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Similarity based functionalization for enumeration of synthetically plausible chemical libraries surrounding a target.

ABSTRACT: Functionalization of lead compounds to create analogs is a challenging step in discovering new molecules with desired properties and it is conducted throughout the chemical industry, including pharmaceuticals and agrochemicals. The process can be time-consuming and expensive, requiring expert intuition and experience. To help address synthesis planning challenges in late-stage functionalization, we have developed a molecular similarity approach that proposes single-step functionalization reactions based on analogy to precedent reactions. The developed approach mimics reaction strategies and suggests co-reactants defined implicitly by a corpus of known reactions. Using ca. 348 k reactions from the patent literature as a knowledge base, the recorded products or close analogs are among the top 20 proposed products in 74% of ∼44 k test reactions. The combinatorial growth inherent in recursive applications of the tool allows the enumeration of chemical libraries surrounding a target compound of interest. Moreover, each step of the resulting library synthesis leverages common chemical transformations reported in the literature accessible to most chemists.

SUBMITTER: Sankaranarayanan K

PROVIDER: S-EPMC11220589 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Similarity based functionalization for enumeration of synthetically plausible chemical libraries surrounding a target.

Sankaranarayanan Karthik K Jensen Klavs F KF

Chemical science 20240524 26

Functionalization of lead compounds to create analogs is a challenging step in discovering new molecules with desired properties and it is conducted throughout the chemical industry, including pharmaceuticals and agrochemicals. The process can be time-consuming and expensive, requiring expert intuition and experience. To help address synthesis planning challenges in late-stage functionalization, we have developed a molecular similarity approach that proposes single-step functionalization reactio ...[more]

PMID: 38966353

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Project description:We report on the development of a cheminformatics enumeration technology and the analysis of a resulting large dataset of virtual macrolide scaffolds. Although macrolides have been shown to have valuable biological properties, there is no ready-to-screen virtual library of diverse macrolides in the public domain. Conducting molecular modeling (especially virtual screening) of these complex molecules is highly relevant as the organic synthesis of these compounds, when feasible, typically requires many synthetic steps, and thus dramatically slows the discovery of new bioactive macrolides. Herein, we introduce a cheminformatics approach and associated software that allows for designing and generating libraries of virtual macrocycle/macrolide scaffolds with user-defined constitutional and structural constraints (e.g., types and numbers of structural motifs to be included in the macrocycle, ring size, maximum number of compounds generated). To study the chemical diversity of such generated molecules, we enumerated V1M (Virtual 1 million Macrolide scaffolds) library, each containing twelve common structural motifs. For each macrolide scaffold, we calculated several key properties, such as molecular weight, hydrogen bond donors/acceptors, topological polar surface area. In this study, we discuss (1) the initial concept and current features of our PKS (polyketides) Enumerator software, (2) the chemical diversity and distribution of structural motifs in V1M library, and (3) the unique opportunities for future virtual screening of such enumerated ensembles of macrolides. Importantly, V1M is provided in the Supplementary Material of this paper allowing other researchers to conduct any type of molecular modeling and virtual screening studies. Therefore, this technology for enumerating extremely large libraries of macrolide scaffolds could hold a unique potential in the field of computational chemistry and drug discovery for rational designing of new antibiotics and anti-cancer agents.

Project description:The prediction of biological targets of bioactive molecules from machine-readable materials can be routinely performed by computational target prediction tools (CTPTs). However, the prediction of biological targets of bioactive molecules from non-digital materials (e.g., printed or handwritten documents) has not been possible due to the complex nature of bioactive molecules and impossibility of employing computations. Improving the target prediction accuracy is the most important challenge for computational target prediction. A minimum structure is identified for each group of neighbor molecules in the proposed method. Each group of neighbor molecules represents a distinct structural class of molecules with the same function in relation to the target. The minimum structure is employed as a query to search for molecules that perfectly satisfy the minimum structure of what is guessed crucial for the targeted activity. The proposed method is based on chemical similarity, but only molecules that perfectly satisfy the minimum structure are considered. Structurally related bioactive molecules found with the same minimum structure were considered as neighbor molecules of the query molecule. The known target of the neighbor molecule is used as a reference for predicting the target of the neighbor molecule with an unknown target. A lot of information is needed to identify the minimum structure, because it is necessary to know which part(s) of the bioactive molecule determines the precise target or targets responsible for the observed phenotype. Therefore, the predicted target based on the minimum structure without employing the statistical significance is considered as a reliable prediction. Since only molecules that perfectly (and not partly) satisfy the minimum structure are considered, the minimum structure can be used without similarity calculations in non-digital materials and with similarity calculations (perfect similarity) in machine-readable materials. Nine tools (PASS online, PPB, SEA, TargetHunter, PharmMapper, ChemProt, HitPick, SuperPred, and SPiDER), which can be used for computational target prediction, are compared with the proposed method for 550 target predictions. The proposed method, SEA, PPB, and PASS online, showed the best quality and quantity for the accurate predictions.

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Similarity based functionalization for enumeration of synthetically plausible chemical libraries surrounding a target.

Publications

Similarity based functionalization for enumeration of synthetically plausible chemical libraries surrounding a target.

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