Molecular Filters in Medicinal Chemistry

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1		Marko Jukic	--	816	2023-04-24 02:33:58	\|
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3	formatted	Vicky Zhou	Meta information modification	816	2023-04-24 04:19:29	\|

Peer Reviewed

This is a peer-reviewed entry published in Encyclopedia Journal, full entry can be found at 10.3390/encyclopedia3020035

Efficient chemical library design for high-throughput virtual screening and drug design requires a pre-screening filter pipeline capable of labeling aggregators, pan-assay interference compounds (PAINS), and rapid elimination of swill (REOS); identifying or excluding covalent binders; flagging moieties with specific bio-evaluation data; and incorporating physicochemical and pharmacokinetic properties early in the design without compromising the diversity of chemical moieties present in the library. This adaptation of the chemical space results in greater enrichment of hit lists, identified compounds with greater potential for further optimization, and efficient use of computational time. A number of medicinal chemistry filters have been implemented in the Konstanz Information Miner (KNIME) software and analyzed their impact on testing representative libraries with chemoinformatic analysis. It was found that the analyzed filters can effectively tailor chemical libraries to a lead-like chemical space, identify protein–protein inhibitor-like compounds, prioritize oral bioavailability, identify drug-like compounds, and effectively label unwanted scaffolds or functional groups. However, one should be cautious in their application and carefully study the chemical space suitable for the target and general medicinal chemistry campaign, and review passed and labeled compounds before taking further in silico steps.

medicinal chemistry filtering chemical libraries chemical space HTVS virtual screening computer aided drug-design in silico drug design bioinformatics chemoinformatics compound library

Physical screening of large libraries was the predominant method for initial steps in drug discovery in the past, and is now effectively complemented by in silico counterpart, namely, HTVS or high-throughput virtual screening ^[1]. Successful virtual screening campaigns can achieve high confirmed hit rates and such methods are gaining in strength with hardware and software development ^[2]. The use of virtual screening on virtual compound libraries reduces the number of potential lead molecules to be evaluated in vitro, increasing time- and cost-efficiency of the drug development process ^[3]. The use of virtual compound libraries brings with it the vast expansion of chemical space that can be searched ^[4]. If conventional physical libraries of pharmaceutical companies are on the scale of 10⁶ to 10⁷ compounds, advanced virtual compound libraries such as GDB-17 can reach up to 1.16¹¹ ^[5]^[6]^[7]^[8]. Despite its sheer size, GDB-17 consists only of molecules with up to 17 atoms of C, N, O, S, and halogens, which points to the fact that the number of possible unique organic molecules is immense, with estimates from 10¹³ to 10¹⁸⁰, depending on inclusion criteria ^[8]^[9]. Even with the rapid development of both computational power in the form of super-computers (HPC) and advances in the methods used, it is impossible to search such vast chemical spaces ^[3]. Such libraries force medicinal chemists to make the trade-off between completeness and screenability, as complete libraries are not easily screened, but screenable libraries are not complete, and could perhaps cover only specific chemical spaces ^[3].

Methods such as molecular docking used for lead identification and molecular dynamics simulations for lead optimization require vast libraries to be processed and focused, as time consumption associated with such methods is far greater than that of simple two-dimensional methods ^[10]. With computer-aided drug design (CADD) the general workflow follows three steps: (1) filtering of large compound libraries into focused libraries based on the users need, (2) discovery and optimization of lead compounds, and (3) development of novel compounds, with steps 2 and 3 repeated until compounds with desired properties are obtained ^[11]^[12]. Since hit rates of screening campaigns are on average as low as 1%, the most efficient and quick way to increase hit-rates is to use molecular filters (or cheminformatics filters) ^[13]. Molecular filters narrow down the chemical space of large libraries towards predetermined goals by removing unwanted chemical structures and properties, with the majority of the filters developed focusing the libraries towards drug-like and bioavailable molecules (Figure 1) ^[4]^[14]. Pioneered by prolific Chris Lipinski and coworkers, molecular filters were developed by intelligent analysis of drug hits obtained in Pfizer’s laboratories, with the assumption that poor physio-chemical properties predominate in many compounds that enter but fail during pre-clinical stages and Phase 1 safety evaluations. By analyzing data from 2245 compounds, they were able to determine molecular features shared among orally available drugs, that critically influence pharmacokinetics ^[14]^[15]. The term drug-likeness, often associated with the use of filters and used in different ways by different authors, generally refers to compounds with desirable properties, such as oral bioavailability, low toxicity, suitable clearance rate, and membrane permeability, which are properties often found in the majority of approved drugs ^[14]^[16]^[17]^[18]. An alternative for narrowing the chemical space of compound libraries is clustering, an approach based on the premise that similar compounds have similar activity. Unlike molecular filtering, clustering works in a less focused way, as compounds are separated based on similarity with a selection of representative compounds from subsequent groups ^[19]. However, unlike molecular filtering, where library size does not impact the choice of the filter used, the choice of the clustering approach is library size dependent. Hierarchical clustering is preferred for small libraries and faster non-hierarchical clustering is preferred for large libraries ^[19]^[20].

Figure 1. Impact of filtering on the size and chemical space of a large and diverse library.

References

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Subjects: Chemistry, Medicinal

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Sebastjan Kralj

Marko Jukič

Urban Bren

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Online Date: 24 Apr 2023

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