Comparison of 2D fingerprint methods for multiple-template similarity searching on compound activity classes of increasing structural diversity

We studied the similarity search performance of differently designed molecular fingerprints using multiple reference structures and different search strategies. For this purpose, nine compound activity classes were assembled that exclusively consisted of molecules with different core structures and that represented different levels of intra-class structural diversity. Thus, there was a strict one-to-one correspondence between test compounds and core structures. Analysis of unique core structures was found to be a better measure of class diversity than distributions of simplified scaffolds. On increasingly diverse classes, a trainable fingerprint using a unique search strategy performed better than others tested herein. Overall, clear preferences were detected for nearest-neighbor search strategies over fingerprint-averaging techniques. Nearest-neighbor searching that relied on selecting database compounds most similar to one of the reference structures often improved compound recovery over other averaging methods, but at the cost of decreasing the ability to detect hits that were structurally distinct from reference molecules.     

A Virtual Screening Study of the 18 kDa Translocator Protein using Pharmacophore Models Combined with 3D‐QSAR Studies

In the present study, we considered various pharmacophore hypotheses for TSPO ligands and an optimal one was selected on the basis of 3D‐QSAR studies. This hypothesis was used in a ligand‐based virtual screening study on the Maybridge database with the aim of identifying new TSPO ligands. Binding assays revealed that all selected compounds displayed TSPO affinity at 10 μM , and among them two compounds exhibited sub‐micromolar K_i values. These results validated our applied methodologies, and the two compounds with sub‐micromolar affinity could be used as interesting leads for the development of new active TSPO ligands.     

Computer-aided evaluation and exploration of chemical spaces constrained by reaction pathways

The processes of molecular design and synthetic route selection are necessarily intertwined during discovery. Computational tools have been developed to facilitate synthesis planning, but in a discovery setting, finding a single route to a single molecule of interest may be less important than finding a route that enables rapid access to a library of analogs. Here, we demonstrate how we can estimate route “diversifiability” and use it as a criterion during route selection. We illustrate how the chemical space of synthetically accessible analogs is influenced by properties of alternative starting materials or constraints on their cost. Finally, we integrate these analyses with a synthesizability-constrained hit expansion workflow in a virtual screening pipeline for focused library expansion around putative hits to support molecular optimization. As medicinal chemistry and adjacent fields shift toward more autonomous design and synthesis of new molecules, it will be increasingly important to embed considerations of synthesizability into molecular design to ensure that computational recommendations are actionable.     

Inhibition of Eimeria tenella CDK‐Related Kinase 2: From Target Identification to Lead Compounds

Apicomplexan parasites encompass several human‐ and animal‐pathogenic protozoans such as Plasmodium falciparum, Toxoplasma gondii, and Eimeria tenella. E. tenella causes coccidiosis, a disease that afflicts chickens, leading to tremendous economic losses to the global poultry industry. The considerable increase in drug resistance makes it necessary to develop new therapeutic strategies against this parasite. Cyclin‐dependent kinases (CDKs) are key molecules in cell‐cycle regulation and are therefore prominent target proteins in parasitic diseases. Bioinformatics analysis revealed four potential CDK‐like proteins, of which one—E. tenella CDK‐related kinase 2 (EtCRK2)—has already been characterized by gene cloning and expression. 1 By using the CDK‐specific inhibitor flavopiridol in EtCRK2 enzyme assays and schizont maturation assays (SMA), we could chemically validate CDK‐like proteins as potential drug targets. An X‐ray crystal structure of human CDK2 (HsCDK2) served as a template to build protein models of EtCRK2 by comparative homology modeling. Structural differences in the ATP binding site between EtCRK2 and HsCDK2, as well as chicken CDK3, were addressed for the optimization of selective ATP‐competitive inhibitors. Virtual screening and “wet‐bench” high‐throughput screening campaigns on large compound libraries resulted in an initial set of hit compounds. These compounds were further analyzed and characterized, leading to a set of four promising lead compounds for development as EtCRK2 inhibitors.     

Comparing Drug Images and Repurposing Drugs with BioGPS and FLAPdock: The Thymidylate Synthase Case

Repurposing and repositioning drugs has become a frequently pursued and successful strategy in the current era, as new chemical entities are increasingly difficult to find and get approved. Herein we report an integrated BioGPS/FLAPdock pipeline for rapid and effective off‐target identification and drug repurposing. Our method is based on the structural and chemical properties of protein binding sites, that is, the ligand image, encoded in the GRID molecular interaction fields (MIFs). Protein similarity is disclosed through the BioGPS algorithm by measuring the pockets’ overlap according to which pockets are clustered. Co‐crystallized and known ligands can be cross‐docked among similar targets, selected for subsequent in vitro binding experiments, and possibly improved for inhibitory potency. We used human thymidylate synthase (TS) as a test case and searched the entire RCSB Protein Data Bank (PDB) for similar target pockets. We chose casein kinase IIα as a control and tested a series of its inhibitors against the TS template. Ellagic acid and apigenin were identified as TS inhibitors, and various flavonoids were selected and synthesized in a second‐round selection. The compounds were demonstrated to be active in the low‐micromolar range.     

Shape Similarity by Fractal Dimensionality: An Application in the de novo Design of (−)-Englerin A Mimetics

Molecular shape and pharmacological function are interconnected. To capture shape, the fractal dimensionality concept was employed, providing a natural similarity measure for the virtual screening of de novo generated small molecules mimicking the structurally complex natural product (−)-englerin A. Two of the top-ranking designs were synthesized and tested for their ability to modulate transient receptor potential (TRP) cation channels which are cellular targets of (−)-englerin A. Intracellular calcium assays and electrophysiological whole-cell measurements of TRPC4 and TRPM8 channels revealed potent inhibitory effects of one of the computer-generated compounds. Four derivatives of this identified hit compound had comparable effects on TRPC4 and TRPM8. The results of this study corroborate the use of fractal dimensionality as an innovative shape-based molecular representation for molecular scaffold-hopping.     

Smart Design of Small-Molecule Libraries: When Organic Synthesis Meets Cheminformatics

Synthetic chemists are always looking for new methods to maximize the diversity and complexity of small-molecule libraries. Diversity-oriented synthesis can give access to new chemotypes with high chemical diversity, exploiting complexity-generating reactions and divergent approaches. However, there is a need for new tools to drive synthetic efforts towards unexplored and biologically relevant regions of chemical space. Because the number of publicly accessible biological data will increase in the years to come, cheminformatics can represent a real opportunity to develop better chemical libraries. This minireview focuses on novel cheminformatics approaches used to design molecular scaffolds, as well as to analyze their quality, giving a perspective of them in the field of chemical biology and drug discovery through some selected case studies.     

Toward Docking‐Based Virtual Screening for Discovering Antitubulin Agents by Targeting Taxane and Colchicine Binding Sites

Docking‐based virtual screening : Flexible docking, scoring, and virtual screening of ligand databases are on the way to fulfilling the promise. Docking‐based virtual screening that targets taxane and colchicine binding sites will certainly provide new antitubulin agents.

     

New inhibitors of the Tat-TAR RNA interaction found with a "fuzzy" pharmacophore model

TAR RNA is a potential target for AIDS therapy. Ligand-based virtual screening was performed to retrieve novel scaffolds for RNA-binding molecules capable of inhibiting the Tat-TAR interaction, which is essential for HIV replication. We used a "fuzzy" pharmacophore approach (SQUID) and an alignment-free pharmacophore method (CATS3D) to carry out virtual screening of a vendor database of small molecules and to perform "scaffold-hopping". A small subset of 19 candidate molecules were experimentally tested for TAR RNA binding in a fluorescence resonance energy transfer (FRET) assay. Both methods retrieved molecules that exhibited activities comparable to those of the reference molecules acetylpromazine and chlorpromazine, with the best molecule showing ten times better binding behavior (IC50 = 46 microM). The hits had molecular scaffolds different from those of the reference molecules.     

Identification of Synthetic Activators of Cancer Cell Migration by Hybrid Deep Learning

Deep convolutional neural networks (CNNs) are a method of choice for image recognition. Herein a hybrid CNN approach is presented for molecular pattern recognition in drug discovery. Using self-organizing map images of molecular pharmacophores as input, CNN models were trained to identify chemokine receptor CXCR4 modulators with high accuracy. This machine learning classifier identified first-in-class synthetic CXCR4 full agonists. The receptor-activating effects were confirmed by intracellular cAMP response and in a phenotypic spheroid invasion assay of medulloblastoma cell invasion. Additional macromolecular targets of the small molecules were predicted in silico and tested in vitro, revealing modulatory effects on dopamine receptors and CCR1. These results positively advocate the applicability of molecular image recognition by CNNs to ligand-based virtual compound screening, and demonstrate the complementarity of machine intelligence and human expert knowledge.     

A Structure‐Based Virtual Screening Approach toward the Discovery of Histone Deacetylase Inhibitors: Identification of Promising Zinc‐Chelating Groups

The inhibitors of histone deacetylases (HDACs) have drawn a great deal of attention due to their promising potential as small‐molecule therapeutics for the treatment of cancer. By means of virtual screening with docking simulations under consideration of the effects of ligand solvation, we were able to identify six novel HDAC inhibitors with IC₅₀ values ranging from 1 to 100 μM . These newly identified inhibitors are structurally diverse and have various chelating groups for the active site zinc ion, including N‐[1,3,4]thiadiazol‐2‐yl sulfonamide, N‐thiazol‐2‐yl sulfonamide, and hydroxamic acid moieties. The former two groups are included in many drugs in current clinical use and have not yet been reported as HDAC inhibitors. Therefore, they can be considered as new inhibitor scaffolds for the development of anticancer drugs by structure–activity relationship studies to improve the inhibitory activities against HDACs. Interactions with the HDAC1 active site residues responsible for stabilizing these new inhibitors are addressed in detail.     

Quantitative Estimate Index for Early-Stage Screening of Compounds Targeting Protein-Protein Interactions

Drug-likeness quantification is useful for screening drug candidates. Quantitative estimates of drug-likeness (QED) are commonly used to assess quantitative drug efficacy but are not suitable for screening compounds targeting protein-protein interactions (PPIs), which have recently gained attention. Therefore, we developed a quantitative estimate index for compounds targeting PPIs (QEPPI), specifically for early-stage screening of PPI-targeting compounds. QEPPI is an extension of the QED method for PPI-targeting drugs that models physicochemical properties based on the information available for drugs/compounds, specifically those reported to act on PPIs. FDA-approved drugs and compounds in iPPI-DB, which comprise PPI inhibitors and stabilizers, were evaluated using QEPPI. The results showed that QEPPI is more suitable than QED for early screening of PPI-targeting compounds. QEPPI was also considered an extended concept of the “Rule-of-Four” (RO4), a PPI inhibitor index. We evaluated the discriminatory performance of QEPPI and RO4 for datasets of PPI-target compounds and FDA-approved drugs using F-score and other indices. The F-scores of RO4 and QEPPI were 0.451 and 0.501, respectively. QEPPI showed better performance and enabled quantification of drug-likeness for early-stage PPI drug discovery. Hence, it can be used as an initial filter to efficiently screen PPI-targeting compounds.