A Comparison of Field-Based Similarity Searching Methods: CatShape, FBSS, and ROCS

Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence

G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand–receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs.

Design, synthesis and biological activities of benzo[d]imidazo[1,2-a]imidazole derivatives as TRPM2-specfic inhibitors

Transient receptor potential melastatin 2 (TRPM2) channel is associated with ischemia/reperfusion injury, inflammation, cancer and neurodegenerative diseases. However, the lack of specific inhibitors impedes the development of TRPM2 targeted therapeutic agents. To develop a selective TRPM2 inhibitor, three-dimensional similarity-based screening strategy was employed using the energy-minimized conformation of non-selective TRPM2 inhibitor 2-APB as the query structure, which resulted in the discovery of a novel tricyclic TRPM2 inhibitor Z-4 with benzo[d]imidazo[1,2-a]imidazole skeleton. A series of Z-4 derivatives were subsequently synthesized and evaluated using calcium imaging and electrophysiology approaches. Among them, preferred compounds ZA10 and ZA18 inhibited the TRPM2 channel with micromolar half-maximal inhibitory concentration values and exhibited TRPM2 selectivity over the TRPM8 channel, TRPV1 channel, InsP₃ receptor and Orai channel. The analysis of structure-activity relationship provides valuable insights for further development of selective TRPM2 inhibitors. Neuroprotection assay showed that ZA10 and ZA18 could effectively reduce the mortality of SH-SY5Y cells induced by H₂O₂. These findings enrich the structure types of existing TRPM2 inhibitors and might provide a new tool for the study of TRPM2 function in Reactive oxygen species (ROS) -related diseases.

Property-Unmatched Decoys in Docking Benchmarks

Enrichment of ligands versus property-matched decoys is widely used to test and optimize docking library screens. However, the unconstrained optimization of enrichment alone can mislead, leading to false confidence in prospective performance. This can arise by over-optimizing for enrichment against property-matched decoys, without considering the full spectrum of molecules to be found in a true large library screen. Adding decoys representing charge extrema helps mitigate over-optimizing for electrostatic interactions. Adding decoys that represent the overall characteristics of the library to be docked allows one to sample molecules not represented by ligands and property-matched decoys but that one will encounter in a prospective screen. An optimized version of the DUD-E set (DUDE-Z), as well as Extrema and sets representing broad features of the library (Goldilocks), is developed here. We also explore the variability that one can encounter in enrichment calculations and how that can temper one's confidence in small enrichment differences. The new tools and new decoy sets are freely available at http://tldr.docking.org and http://dudez.docking.org.

Maximum common property: a new approach for molecular similarity

The maximum common property similarity (MCPhd) method is presented using descriptors as a new approach to determine the similarity between two chemical compounds or molecular graphs. This method uses the concept of maximum common property arising from the concept of maximum common substructure and is based on the electrotopographic state index for atoms. A new algorithm to quantify the similarity values of chemical structures based on the presented maximum common property concept is also developed in this paper. To verify the validity of this approach, the similarity of a sample of compounds with antimalarial activity is calculated and compared with the results obtained by four different similarity methods: the small molecule subgraph detector (SMSD), molecular fingerprint based (OBabel_FP2), ISIDA descriptors and shape-feature similarity (SHAFTS). The results obtained by the MCPhd method differ significantly from those obtained by the compared methods, improving the quantification of the similarity. A major advantage of the proposed method is that it helps to understand the analogy or proximity between physicochemical properties of the molecular fragments or subgraphs compared with the biological response or biological activity. In this new approach, more than one property can be potentially used. The method can be considered a hybrid procedure because it combines descriptor and the fragment approaches.

Virtual Screening Meets Deep Learning

BACKGROUND

Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence.

OBJECTIVE

This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

Shape-based virtual screen for the discovery of novel CDK8 inhibitor chemotypes

With the aim of discovering novel cyclin-dependent kinase 8 (CDK8) inhibitors, a combined similarity search and molecular docking approach was employed, which led to 32 hits. Biological tests led to the discovery of several novel submicromolar inhibitors. In particular, compound C768-0769 (ZC0201) showed good CDK8 inhibitory activity, and compound ZC0201 effectively suppressed HCT-116 colorectal cancer cell proliferation by inducing G1/S transition arrest. Furthermore, modulation of phosphorylated signal transducer and activator of transcription 1 (Ser 727) (STAT1^SER727), a pharmacodynamic biomarker of CDK8 activity, demonstrated that ZC0201 may cause G1/S transition arrest through CDK8 activity inhibition. Due to its good cellular activity, ZC0201 may be an ideal lead compound for further modification as a potential cancer therapeutic agent.

Discovery, design and synthesis of 6H-anthra[1,9-cd]isoxazol-6-one scaffold as G9a inhibitor through a combination of shape-based virtual screening and structure-based molecular modification

Protein lysine methyltransferase G9a is widely considered as an appealing antineoplastic target. Herein we present an integrated workflow combining shape-based virtual screening and structure-based molecular modification for the identification of novel G9a inhibitors. The shape-based similarity screening through ROCS overlay on the basis of the structure of UNC0638 was performed to identify CPUY074001 contained a 6H-anthra[1,9-cd]isoxazol-6-one scaffold as a hit. Analysis of the binding mode of CPUY074001 with G9a and 3D-QSAR results, two series compounds were designed and synthesized. The derivatives were confirmed to be active by in vitro assay and the SAR was explored by docking stimulations. Besides, several analogues showed acceptable anti-proliferative effects against several cancer cell lines. Among them, CPUY074020 displayed potent dual G9a inhibitory activity and anti-proliferative activity. Furthermore, CPUY074020 induced cell apoptosis in a dose-dependent manner and displayed a significant decrease in dimethylation of H3K9. Simultaneously, CPUY074020 showed reasonable in vivo PK properties. Altogether, our workflow supplied a high efficient strategy in the identification of novel G9a inhibitors. Compounds reported here can serve as promising leads for further study.

Identification of potential CCR5 inhibitors through pharmacophore-based virtual screening, molecular dynamics simulation and binding free energy analysis

Employing the combined strategy to identify novel CCR5 inhibitors and provide a basis for rational drug design.

Discovery of NAD(P)H:quinone oxidoreductase 1 (NQO1) inhibitors with novel chemical scaffolds by shape-based virtual screening combined with cascade docking

A number of novel NAD(P)H:quinone oxidoreductase 1 (NQO1) inhibitors were discovered from the ChemDiv database via a simple protocol.

Discovery and Bioevaluation of Novel Pyrazolopyrimidine Analogs as Competitive Hsp90 Inhibitors Through Shape-Based Similarity Screening

Hsp90 as a promising therapeutic target for the treatment of cancer has received great attention. Many Hsp90 inhibitors such as BIIB021 and CUDC-305 have been in clinical. In this paper shape-based similarity screening through ROCS overlays on the basis of CUDC-305, BIIB021, PU-H71 and PU-3 were performed to discover HSP90 inhibitors. A set of 19 novel pyrazolopyrimidine analogues was identified and evaluated on enzyme level and cell-based level as Hsp90 inhibitors. The compound HDI4-04 with IC50 0.35 µM in the Hsp90 ATP hydrolysis assay exhibited potent cytotoxicity against five human cancer cell lines. Western blot analysis and Hsp70 luciferase reporter assay further confirmed that HDI4-04 targeted the Hsp90 protein folding machinery. And according to the biological assay, the SAR was discussed and summarized, which will guide us for further optimization of these compounds.

Molecular similarity in medicinal chemistry

Similarity is a subjective and multifaceted concept, regardless of whether compounds or any other objects are considered. Despite its intrinsically subjective nature, attempts to quantify the similarity of compounds have a long history in chemical informatics and drug discovery. Many computational methods employ similarity measures to identify new compounds for pharmaceutical research. However, chemoinformaticians and medicinal chemists typically perceive similarity in different ways. Similarity methods and numerical readouts of similarity calculations are probably among the most misunderstood computational approaches in medicinal chemistry. Herein, we evaluate different similarity concepts, highlight key aspects of molecular similarity analysis, and address some potential misunderstandings. In addition, a number of practical aspects concerning similarity calculations are discussed.

Discovery and design of tricyclic scaffolds as protein kinase CK2 (CK2) inhibitors through a combination of shape-based virtual screening and structure-based molecular modification

Protein kinase CK2 (CK2), a ubiquitous serine/threonine protein kinase for hundreds of endogenous substrates, serves as an attractive anticancer target. One of its most potent inhibitors, CX-4945, has entered a phase I clinical trial. Herein we present an integrated workflow combining shape-based virtual screening for the identification of novel CK2 inhibitors. A shape-based model derived from CX-4945 was built, and the subsequent virtual screening led to the identification of several novel scaffolds with high shape similarity to that of CX-4945. Among them two tricyclic scaffolds named [1,2,4]triazolo[4,3-c]quinazolin and [1,2,4]triazolo[4,3-a]quinoxalin attracted us the most. Combining strictly chemical similarity analysis, a second-round shape-based screening was performed based on the two tricyclic scaffolds, leading to 28 derivatives. These compounds not only targeted CK2 with potent and dose-dependent activities but also showed acceptable antiproliferative effects against a series of cancer cell lines. Our workflow supplies a high efficient strategy in the identification of novel CK2 inhibitors. Compounds reported here can serve as ideal leads for further modifications.

What has virtual screening ever done for drug discovery?

BACKGROUND

Although virtual screening is now widely applied as a hit-finding methodology within drug discovery programmes, there are relatively few reports of its contributing to compounds on the market or in the clinic.

OBJECTIVE

To assess the impact of virtual screening on drug discovery.

METHOD

Such cases as can be found in the public domain at the current time are reviewed. Additionally, some of the current challenges in structure- and ligand-based virtual screening are discussed.

CONCLUSION

It is concluded that virtual screening has contributed to the discovery of several compounds that have either reached the market or entered clinical trials. In terms of praxis, there is 'no free lunch' in virtual screening and as many methods as possible should be applied to maximise the likelihood of success.

Identification of HIV-1 reverse transcriptase dual inhibitors by a combined shape-, 2D-fingerprint- and pharmacophore-based virtual screening approach

We report the first application of ligand-based virtual screening (VS) methods for discovering new compounds able to inhibit both human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT)-associated functions, DNA polymerase and ribonuclease H (RNase H) activities. The overall VS campaign consisted of two consecutive screening processes. In the first, the VS platform Rapid Overlay of Chemical Structures (ROCS) was used to perform in silico shape-based similarity screening on the NCI compounds database in which a hydrazone derivative, previously shown to inhibit the HIV-1 RT, was chosen. As a result, 34 hit molecules were selected and assayed on both RT-associated functions. In the second, the 4 most potent RT inhibitors identified were selected as queries for parallel VS performed by combining shape-based, 2D-fingerprint and 3D-pharmacophore VS methods. Overall, a set of molecules characterized by new different scaffolds were identified as novel inhibitors of both HIV-1 RT-associated activities in the low micromolar range.

Graph-based similarity concepts in virtual screening

Applying similarity for finding new promising compounds is a key issue in drug design. Conversely, quantifying similarity between molecules has remained a difficult task despite the numerous approaches. Here, some general aspects along with recent developments regarding similarity criteria are collected. For the purpose of virtual screening, the compounds have to be encoded into a computer-readable format that permits a comparison, according to given similarity criteria, comprising the use of the 3D structure, fingerprints, graph-based and alignment-based approaches. Whereas finding the most common substructures is the most obvious method, more recent approaches take into account chemical modifications that appear throughout existing drugs, from various therapeutic categories and targets.

Application of QSAR and shape pharmacophore modeling approaches for targeted chemical library design

Optimization of chemical library composition affords more efficient identification of hits from biological screening experiments. The optimization could be achieved through rational selection of reagents used in combinatorial library synthesis. However, with a rapid advent of parallel synthesis methods and availability of millions of compounds synthesized by many vendors, it may be more efficient to design targeted libraries by means of virtual screening of commercial compound collections. This chapter reviews the application of advanced cheminformatics approaches such as quantitative structure-activity relationships (QSAR) and pharmacophore modeling (both ligand and structure based) for virtual screening. Both approaches rely on empirical SAR data to build models; thus, the emphasis is placed on achieving models of the highest rigor and external predictive power. We present several examples of successful applications of both approaches for virtual screening to illustrate their utility. We suggest that the expert use of both QSAR and pharmacophore models, either independently or in combination, enables users to achieve targeted libraries enriched with experimentally confirmed hit compounds.

Comparative evaluation of 3D virtual ligand screening methods: impact of the molecular alignment on enrichment

In the early stage of drug discovery programs, when the structure of a complex involving a target and a small molecule is available, structure-based virtual ligand screening methods are generally preferred. However, ligand-based strategies like shape-similarity search methods can also be applied. Shape-similarity search methods consist in exploring a pseudo-binding-site derived from the known small molecule used as a reference. Several of these methods use conformational sampling algorithms which are also shared by corresponding docking methods: for example Surflex-dock/Surflex-sim, FlexX/FlexS, ICM, and OMEGA-FRED/OMEGA-ROCS. Using 11 systems issued from the challenging "own" subsets of the Directory of Useful Decoys (DUD-own), we evaluated and compared the performance of the above-cited programs in terms of molecular alignment accuracy, enrichment in active compounds, and enrichment in different chemotypes (scaffold-hopping). Since molecular alignment is a crucial aspect of performance for the different methods, we have assessed its impact on enrichment. We have also illustrated the paradox of retrieving active compounds with good scores even if they are inaccurately positioned. Finally, we have highlighted possible positive aspects of using shape-based approaches in drug-discovery protocols when the structure of the target in complex with a small molecule is known.

HPPD: ligand- and target-based virtual screening on a herbicide target

Hydroxyphenylpyruvate dioxygenase (HPPD) has proven to be a very successful target for the development of herbicides with bleaching properties, and today HPPD inhibitors are well established in the agrochemical market. Syngenta has a long history of HPPD-inhibitor research, and HPPD was chosen as a case study for the validation of diverse ligand- and target-based virtual screening approaches to identify compounds with inhibitory properties. Two-dimensional extended connectivity fingerprints, three-dimensional shape-based tools (ROCS, EON, and Phase-shape) and a pharmacophore approach (Phase) were used as ligand-based methods; Glide and Gold were used as target-based. Both the virtual screening utility and the scaffold-hopping ability of the screening tools were assessed. Particular emphasis was put on the specific pitfalls to take into account for the design of a virtual screening campaign in an agrochemical context, as compared to a pharmaceutical environment.

Comparison of structure- and ligand-based virtual screening protocols considering hit list complementarity and enrichment factors

Structure- and ligand-based virtual-screening methods (docking, 2D- and 3D-similarity searching) were analysed for their effectiveness in virtual screening against four different targets: angiotensin-converting enzyme (ACE), cyclooxygenase 2 (COX-2), thrombin and human immunodeficiency virus 1 (HIV-1) protease. The relative performance of the tools was compared by examining their ability to recognise known active compounds from a set of actives and nonactives. Furthermore, we investigated whether the application of different virtual-screening methods in parallel provides complementary or redundant hit lists. Docking was performed with GOLD, Glide, FlexX and Surflex. The obtained docking poses were rescored by using nine different scoring functions in addition to the scoring functions implemented as objective functions in the docking algorithms. Ligand-based virtual screening was done with ROCS (3D-similarity searching), Feature Trees and Scitegic Functional Fingerprints (2D-similarity searching). The results show that structure- and ligand-based virtual-screening methods provide comparable enrichments in detecting active compounds. Interestingly, the hit lists that are obtained from different virtual-screening methods are generally highly complementary. These results suggest that a parallel application of different structure- and ligand-based virtual-screening methods increases the chance of identifying more (and more diverse) active compounds from a virtual-screening campaign.

Improving virtual screening performance against conformational variations of receptors by shape matching with ligand binding pocket

In this report, we present a novel virtual high-throughput screening methodology to assist in computer-aided drug discovery. Our method, designated as SLIM, involves ligand-free shape and chemical feature matching. The procedure takes advantage of a negative image of a binding pocket in a target receptor. The negative image is a set of virtual atoms representing the inner shape and chemical features of the binding pocket. Using this image, SLIM implements a shape-based similarity search based on molecular volume superposition for the ensemble of conformers of each molecule. The superposed structures, prioritized by shape similarity, are subjected to comparison of chemical feature similarities. To validate the merits of the SLIM method, we compared its performance with those of three distinct widely used tools ROCS, GLIDE, and GOLD. ROCS was selected as a representative of the ligand-centric methods, and docking programs GLIDE and GOLD as representatives of the receptor-centric methods. Our data suggest that SLIM has overall hit ranking ability that is comparable to that of the docking method, retaining the high computational speed of the ligand-centric method. It is notable that the SLIM method offers consistently reliable screening quality against conformational variations of receptors, whereas the docking methods have limited screening performance.

Binding Mode Prediction of PDE4 Inhibitors: A Comparison of Modelling Methods

Molecular modelling is widely used in support of medicinal chemistry programs, with several theoretical approaches used in attempts to expedite drug discovery. In this study, three methods – molecular docking (Glide), shape similarity (ROCS), and pharmacophore modelling (Phase) – were evaluated for their ability to reproduce experimentally determined binding modes of 25 PDE4 inhibitors, identified by X-ray crystallography. Molecular docking was able to provide a good approximation (RMSD less than 2 Å) in 59% of cases, when considering the top binding pose. The pairwise comparisons, using molecular shape similarity, gave good matches in 42% of cases. Pharmacophore models were unable to predict good binding modes for a series of PDE4 inhibitors.

The importance of discerning shape in molecular pharmacology

Shape is a fundamentally important molecular feature that often determines the fate of a compound in terms of molecular interactions with preferred and non-preferred biological targets. Complementarity of binding in small-molecule-protein, peptide-receptor, antigen-antibody and protein-protein interactions is the key to life and survival and also to targeting molecules with bioactivity. We review the application of shape in various biological systems such as substrate recognition, ligand specificity or selectivity and antibody recognition in the context of computational methods such as docking, quantitative structure-activity relationships, classification models and similarity-search algorithms. These in silico pharmacology methods have recently demonstrated the importance and applicability of determining molecular shape in drug discovery, virtual screening and predictive toxicology. The results from recently published studies show that shape and shape-based descriptors are at least as useful as other traditional molecular descriptors.