Identification of “Latent Hits” in Compound Screening Collections

Journal of Chemical Information and Modeling, 2020

Large scale in vitro and in silico screening are two orthogonal approaches for hit identification in drug discovery. In recent years, due to the emergence of new targets and a rapid increase in the size of the readily synthesizable chemical space, there is a growing emphasis on the integration of the two techniques to improve the hit finding efficiency. Here, we highlight three examples of drug discovery projects at Merck & Co., Inc., Kenilworth, NJ, USA in which different virtual screening (VS) techniques, each specifically tailored to leverage knowledge available for the target, were utilized to augment the selection of high-quality chemical matter for in vitro assays and to enhance the diversity and tractability of hits. Central to success is a fully integrated workflow combining in silico and experimental expertise at every stage of the hit identification process. We advocate

Current Drug Discovery Technologies, 2004

Each year large pharmaceutical companies produce massive amounts of primary screening data for lead discovery. To make better use of the vast amount of information in pharmaceutical databases, companies have begun to scrutinize the lead generation stage to ensure that more and better qualified lead series enter the downstream optimization and development stages. This article describes computational techniques for end to end analysis of large drug discovery screening sets. The analysis proceeds in three stages: In stage 1 the initial screening set is filtered to remove compounds that are unsuitable as lead compounds. In stage 2 local structural neighborhoods around active compound classes are identified, including similar but inactive compounds. In stage 3 the structure-activity relationships within local structural neighborhoods are analyzed. These processes are illustrated by analyzing two large, publicly available databases.

IBM Systems Journal, 2001

Virtual screening, or in silico screening, is a new approach attracting increasing levels of interest in the pharmaceutical industry as a productive and cost-effective technology in the search for novel lead compounds. Although the principles involved-the computational analysis of chemical databases to identify compounds appropriate for a given biological receptor-have been pursued for several years in molecular modeling groups, the availability of inexpensive high-performance computing platforms has transformed the process so that increasingly complex and more accurate analyses can be performed on very large data sets. The virtual screening technology of Protherics Molecular Design Ltd. is based on its integrated software environment for receptorbased drug design, called Prometheus. In particular, molecular docking is used to predict the binding modes and binding affinities of every compound in the data set to a given biological receptor. This method represents a very detailed and relevant basis for prioritizing compounds for biological screening. This paper discusses the broader scope of virtual screening and, as an example, describes our recent work in docking one million compounds into the estrogen hormone receptor in order to highlight the technical feasibility of performing very largescale virtual screening as a route to identifying novel drug leads.

Chemical Research in Toxicology, 2006

The physiological roles of estrogen in sexual differentiation and development, female and male reproductive processes, and bone health are complex and diverse. Numerous natural and synthetic chemical compounds, commonly known as endocrine disrupting chemicals (EDCs), have been shown to alter the physiological effects of estrogen in humans and wildlife. As such, these EDCs may cause unanticipated and even undesirable effects. Large-scale in vitro and in vivo screening of chemicals to assess their estrogenic activity would demand a prodigious investment of time, labor, and money and would require animal testing on an unprecedented scale. Approaches in silico are increasingly recognized as playing a vital role in screening and prioritizing chemicals to extend limited resources available for experimental testing. Here, we evaluated a multistep procedure that is suitable for in silico (virtual) screening of large chemical databases to identify compounds exhibiting estrogenic activity. This procedure incorporates Shape Signatures, a novel computational tool that rapidly compares molecules on the basis of similarity in shape, polarity, and other biorelevant properties. Using 4-hydroxy tamoxifen (4-OH TAM) and diethylstilbestrol (DES) as input queries, we employed this scheme to search a sample database of ~200 000 commercially available organic chemicals for matches (hits). Of the eight compounds identified computationally as potentially (anti)estrogenic, biological evaluation confirmed two as heretofore unknown estrogen antagonists. Subsequent radioligand binding assays confirmed that two of these three compounds exhibit antiestrogenic activities comparable to 4-OH TAM. Molecular modeling studies of these ligands docked inside the binding pocket of estrogen receptor α (ERα) elucidated key ligand-receptor interactions that corroborate these experimental findings. The present study demonstrates the utility of our computational scheme for this and related applications in drug discovery, predictive toxicology, and virtual screening.

Journal of Chemical Information and Modeling, 2005

Structure-based virtual screening (SBVS) utilizing docking algorithms has become an essential tool in the drug discovery process, and significant progress has been made in successfully applying the technique to a wide range of receptor targets. In silico validation of virtual screening protocols before application to a receptor target using a corporate or commercially available compound collection is key to establishing a successful process. Ultimately, retrieval of a set of active compounds from a database of inactives is required, and the metric of enrichment (E) is habitually used to discern the quality of separation of the two. Numerous reports have addressed the performance of docking algorithms with regard to the quality of binding mode prediction and the issue of postprocessing "hit lists" of docked ligands. However, the impact of ligand database preprocessing has yet to be examined in the context of virtual screening and prioritization of compounds for biological evaluation. We provide an insight into the implications of cheminformatic preprocessing of a validation database of compounds where multiple protonated, tautomeric, stereochemical, and conformational states have been enumerated. Several commonly used methods for the generation of ligand conformations and conformational ensembles are examined, paired with an exhaustive rigid-body algorithm for the docking of different "multimeric" compound representations to the ligand binding site of the human estrogen receptor alpha. Chemgauss, a shapegaussian scoring function with intrinsic chemical knowledge, was combined with PLP as a consensus-scoring scheme to rank output from the docking protocol and enrichment rates calculated for each screen. The overheads of CPU consumption and the effect on relative database size (disk requirement) for each of the protocols employed are considered. Assessment of these parameters indicates that SBVS enrichments are highly dependent on the initial cheminformatic treatment(s) used in database construction. The interplay of SMILES representations, stereochemical information, protonation state enumeration, and ligand conformation ensembles are critical in achieving optimum enrichment rates in such screening.

2007

Journal of Medicinal Chemistry, 2007

In this work, we introduce a four-step scoring and filtering procedure, furnishing target specific virtual screening (TS-VS), which serves to minimize false positives resulting from conformational artifacts of the docking process and is optimized to converge on novel chemotypes of estrogen receptor alpha (ERR). As a proof of concept, VS of a commercial compound database was undertaken (SPECs database release: Aug 2005, 202 054 compounds in total), resulting in the identification of both previously known and novel putative ER scaffolds. Application of distance constraints within TS-VS allowed facile identification of three novel active ligands with ERR binding affinities (IC 50 ) of 1.4 µM, 57 nM, and 53 nM. Importantly, they all exhibited ERR over ER selectivity, with the most selective being 17-fold. The ligands also displayed low micomolar antiproliferative activity (7-15 µM) in the human MCF-7 breast cancer cell line.

The purpose of High Throughput Screening (HTS) in pharmaceutical industry is to identify, as soon as possible, compounds that are good starting points for successful new drug development process. Experts from this area study the chemical structures of so called »hit« compounds that have been found to interact with the target protein, interfere with proliferation of different types of cells or stop bacterial or fungal growth. Hypotheses to design related structures with improved biological properties are than builded. Each idea is then tested by the iterative synthesis and testing of novel compounds in various biological assays, searching for hits with better properties and defining useful and promising »lead« molecules. In parallel, molecular modeling and chemoinformatics experts can increase efficiency and decrease experimental costs by using different database filtering methods. In such a way, hits from HTS may be assessed before committing significant resource for chemical optimization. Joint efforts of these HTS experimental and modeling groups are the best way to speed up the process of finding a new useful hits and promising leads.

Archives of Biochemistry and Biophysics, 2015

Computational aided drug design (CADD) is presently a key component in the process of drug discovery 28 and development as it offers great promise to drastically reduce cost and time requirements. 29 In the pharmaceutical arena, virtual screening is normally regarded as the top CADD tool to screen large 30 libraries of chemical structures and reduce them to a key set of likely drug candidates regarding a specific 31 protein target. This chapter provides a comprehensive overview of the receptor-based virtual screening 32 process and of its importance in the present drug discovery and development paradigm. Following a 33 focused contextualization on the subject, the main stages of a virtual screening campaign, including its 34 strengths and limitations, are the subject of particular attention in this review. In all of these stages spe-35 cial consideration will be given to practical issues that are normally the Achilles heel of the virtual screen-36 ing process.

Virtual screening (VS) is a powerful technique for identifying hit molecules as starting points for medicinal chemistry. The number of methods and softwares which use the ligand and target-based VS approaches is increasing at a rapid pace. What, however, are the real advantages and disadvantages of the VS technology and how applicable is it to drug discovery projects? This review provides a comprehensive appraisal of several VS approaches currently available. In the first part of this work, an overview of the recent progress and advances in both ligand-based VS (LBVS) and structurebased VS (SBVS) strategies highlighting current problems and limitations will be provided. Special emphasis will be given to in silico chemogenomics approaches which utilize annotated ligand-target as well as protein-ligand interaction databases and which could predict or reveal promiscuous binding and polypharmacology, the knowledge of which would help medicinal chemists to design more potent clinical candidates with fewer side effects. In the second part, recent case studies (all published in the last two years) will be discussed where the VS technology has been applied successfully. A critical analysis of these case studies provides a good platform in order to estimate the applicability of various VS strategies in the new lead identification and optimization.