Comparison of shape-matching and docking as virtual screening tools

MAGPIE: An interactive tool for visualizing and analyzing protein-ligand interactions

Quantitative tools to compile and analyze biomolecular interactions among chemically diverse binding partners would improve therapeutic design and aid in studying molecular evolution. Here we present Mapping Areas of Genetic Parsimony In Epitopes (MAGPIE), a publicly available software package for simultaneously visualizing and analyzing thousands of interactions between a single protein or small molecule ligand (the "target") and all of its protein binding partners ("binders"). MAGPIE generates an interactive three-dimensional visualization from a set of protein complex structures that share the target ligand, as well as sequence logo-style amino acid frequency graphs that show all the amino acids from the set of protein binders that interact with user-defined target ligand positions or chemical groups. MAGPIE highlights all the salt bridge and hydrogen bond interactions made by the target in the visualization and as separate amino acid frequency graphs. Finally, MAGPIE collates the most common target-binder interactions as a list of "hotspots," which can be used to analyze trends or guide the de novo design of protein binders. As an example of the utility of the program, we used MAGPIE to probe how different antibody fragments bind a viral antigen; how a common metabolite binds diverse protein partners; and how two ligands bind orthologs of a well-conserved glycolytic enzyme for a detailed understanding of evolutionarily conserved interactions involved in its activation and inhibition. MAGPIE is implemented in Python 3 and freely available at https://github.com/glasgowlab/MAGPIE, along with sample datasets, usage examples, and helper scripts to prepare input structures.

Scaffold-Hopped Compound Identification by Ligand-Based Approaches with a Prospective Affinity Test

Scaffold-hopped (SH) compounds are bioactive compounds structurally different from known active compounds. Identifying SH compounds in the ligand-based approaches has been a central issue in medicinal chemistry, and various molecular representations of scaffold hopping have been proposed. However, appropriate representations for SH compound identification remain unclear. Herein, the ability of SH compound identification among several representations was fairly evaluated based on retrospective validation and prospective demonstration. In the retrospective validation, the combinations of two screening algorithms and four two- and three-dimensional molecular representations were compared using controlled data sets for the early identification of SH compounds. We found that the combination of the support vector machine and extended connectivity fingerprint with bond diameter 4 (SVM-ECFP4) and SVM and the rapid overlay of chemical structures (SVM-ROCS) showed a relatively high performance. The compounds that were highly ranked by SVM-ROCS did not share substructures with the active training compounds, while those ranked by SVM-ECFP4 were mostly recombinant. In the prospective demonstration, 93 SH compounds were prepared by screening the Namiki database using SVM-ROCS, targeting ABL1 inhibitors. The primary screening using surface plasmon resonance suggested five active compounds; however, in the competitive binding assays with adenosine triphosphate, no hits were found.

Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery

INTRODUCTION

Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology.

AREAS COVERED

This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples.

EXPERT OPINION

Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.

G Protein-Coupled Estrogen Receptor-Mediated Anti-Inflammatory and Mucosal Healing Activity of a Trimethylpyridinol Analogue in Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is characterized by abnormal immune responses, including elevated proinflammatory cytokines, such as tumor necrosis factor-α (TNFα) and interleukin-6 (IL-6) in the gastrointestinal (GI) tract. This study presents the synthesis and anti-inflammatory evaluation of 2,4,5-trimethylpyridin-3-ol analogues, which exhibit dual inhibition of TNFα- and IL-6-induced inflammation. Analysis using in silico methods, including 3D shape-based target identification, modeling, and docking, identified G protein-coupled estrogen receptor 1 (GPER) as the molecular target for the most effective analogue, 6-26, which exhibits remarkable efficacy in ameliorating inflammation and restoring colonic mucosal integrity. This was further validated by surface plasmon resonance (SPR) assay results, which showed direct binding to GPER, and by the results showing that GPER knockdown abolished the inhibitory effects of 6-26 on TNFα and IL-6 actions. Notably, 6-26 displayed no cytotoxicity, unlike G1 and G15, a well-known GPER agonist and an antagonist, respectively, which induced necroptosis independently of GPER. These findings suggest that the GPER-selective compound 6-26 holds promise as a therapeutic candidate for IBD.

Identification of ligands binding to MB327-PAM-1, a binding pocket relevant for resensitization of nAChRs

Desensitization of nicotinic acetylcholine receptors (nAChRs) can be induced by overstimulation with acetylcholine (ACh) caused by an insufficient degradation of ACh after poisoning with organophosphorus compounds (OPCs). Currently, there is no generally applicable treatment for OPC poisoning that directly targets the desensitized nAChR. The bispyridinium compound MB327, an allosteric modulator of nAChR, has been shown to act as a resensitizer of nAChRs, indicating that drugs binding directly to nAChRs can have beneficial effects after OPC poisoning. However, MB327 also acts as an inhibitor of nAChRs at higher concentrations and can thus not be used for OPC poisoning treatment. Consequently, novel, more potent resensitizers are required. To successfully design novel ligands, the knowledge of the binding site is of utmost importance. Recently, we performed in silico studies to identify a new potential binding site of MB327, MB327-PAM-1, for which a more affine ligand, UNC0646, has been described. In this work, we performed ligand-based screening approaches to identify novel analogs of UNC0646 to help further understand the structure-affinity relationship of this compound class. Furthermore, we used structure-based screenings and identified compounds representing four new chemotypes binding to MB327-PAM-1. One of these compounds, cycloguanil, is the active metabolite of the antimalaria drug proguanil and shows a higher affinity towards MB327-PAM-1 than MB327. Furthermore, cycloguanil can reestablish the muscle force in soman-inhibited rat muscles. These results can act as a starting point to develop more potent resensitizers of nAChR and to close the gap in the treatment after OPC poisoning.

Identification of Novel Potent NSD2-PWWP1 Ligands Using Structure-Based Design and Computational Approaches

Dysregulation of histone methyl transferase nuclear receptor-binding SET domain 2 (NSD2) has been implicated in several hematological and solid malignancies. NSD2 is a large multidomain protein that carries histone writing and histone reading functions. To date, identifying inhibitors of the enzymatic activity of NSD2 has proven challenging in terms of potency and SET domain selectivity. Inhibition of the NSD2-PWWP1 domain using small molecules has been considered as an alternative approach to reduce NSD2-unregulated activity. In this article, we present novel computational chemistry approaches, encompassing free energy perturbation coupled to machine learning (FEP/ML) models as well as virtual screening (VS) activities, to identify high-affinity NSD2 PWWP1 binders. Through these activities, we have identified the most potent NSD2-PWWP1 binder reported so far in the literature: compound 34 (pIC50 = 8.2). The compounds identified herein represent useful tools for studying the role of PWWP1 domains for inhibition of human NSD2.

Development of a novel representation of drug 3D structures and enhancement of the TSR-based method for probing drug and target interactions

Understanding the mechanisms underlying interactions between drugs and target proteins is critical for drug discovery. In our earlier studies, we introduced the Triangular Spatial Relationship (TSR)-based algorithm, which enables the representation of a protein's 3D structure as a vector of integers (TSR keys). These TSR keys correspond to substructures of the 3D structure of a protein and are computed based on the triangles constructed by all possible triples of Cα atoms within the protein. In this study, we report on a new TSR-based algorithm for probing drug and target interactions. Specifically, we have extended the previous algorithm in three novel directions: TSR keys for representing the 3D structure of a drug or a ligand, cross TSR keys between drugs and their targets and intra-residual TSR keys for phosphorylated amino acids. The outcomes illustrate the key contributions as follows: (i) The TSR-based method, which uses the TSR keys as features, is unique in its capability to interpret hierarchical relationships of drugs as well as drug - target complexes using common and specific TSR keys. (ii) The method can distinguish not only the binding sites from the rest of the protein structures, but also the binding sites of primary targets from those of off-targets. (iii) The method has the potential to correlate the 3D structures of drugs with their functions. (iv) Representation of 3D structures by TSR keys has its unique advantage in terms of ease of making searching for similar substructures across structure datasets easier. In summary, this study presents a novel computational methodology, with significant advantages, for providing insights into the mechanism underlying drug and target interactions.

Discovery of a novel chemotype as DYRK1A inhibitors against Alzheimer's disease: Computational modeling and biological evaluation

Dual-specificity tyrosine phosphorylation-regulated kinase 1 A (DYRK1A) plays an essential role in Tau and Aβ pathology closely related to Alzheimer's disease (AD). Accumulative evidence has demonstrated DYRK1A inhibition is able to reduce the pathological features of AD. Nevertheless, there is no approved DYRK1A inhibitor for clinical use as anti-AD therapy. This is somewhat due to the lack of effective and safe chemotypes of DYRK1A inhibitors. To address this issue, we carried out in silico screening, in vitro assays and in vivo efficacy evaluation with the aim to discover a new class of DYRK1A inhibitors for potential treatment of AD. By in silico screening, we selected and purchased 16 potential DYRK1A inhibitors from the Specs chemical library. Among them, compound Q17 (Specs ID: AO-476/40829177) potently inhibited DYRK1A. The hydrogen bonds between compound Q17 and two amino acid residues named GLU239 and LYS188, were uncovered by molecular docking and molecular dynamics simulation. The cell-based assays showed that compound Q17 could protect the SH-SY5Y human neuroblastoma cell line from okadaic acid (OA)-induced injury by targeting DYRK1A. More importantly, compound Q17 significantly improved cognitive dysfunction of 3 × Tg-AD mice, ameliorated pathological changes, and attenuated Tau hyperphosphorylation as well as Aβ deposition. In summary, our computational modeling strategy is effective to identify novel chemotypes of DYRK1A inhibitors with great potential to treat AD, and the identified compound Q17 in this study is worthy of further study.

Ligandability Assessment of IL-1β by Integrated Hit Identification Approaches

Human interleukin-1β (IL-1β) is a pro-inflammatory cytokine that plays a critical role in the regulation of the immune response and the development of various inflammatory diseases. In this publication, we disclose our efforts toward the discovery of IL-1β binders that interfere with IL-1β signaling. To this end, several technologies were used in parallel, including fragment-based screening (FBS), DNA-encoded library (DEL) technology, peptide discovery platform (PDP), and virtual screening. The utilization of distinct technologies resulted in the identification of new chemical entities exploiting three different sites on IL-1β, all of them also inhibiting the interaction with the IL-1R1 receptor. Moreover, we identified lysine 103 of IL-1β as a target residue suitable for the development of covalent, low-molecular-weight IL-1β antagonists.

Selective and brain-penetrant ACSS2 inhibitors target breast cancer brain metastatic cells

Breast cancer brain metastasis (BCBM) typically results in an end-stage diagnosis and is hindered by a lack of brain-penetrant drugs. Tumors in the brain rely on the conversion of acetate to acetyl-CoA by the enzyme acetyl-CoA synthetase 2 (ACSS2), a key regulator of fatty acid synthesis and protein acetylation. Here, we used a computational pipeline to identify novel brain-penetrant ACSS2 inhibitors combining pharmacophore-based shape screen methodology with absorption, distribution, metabolism, and excretion (ADME) property predictions. We identified compounds AD-5584 and AD-8007 that were validated for specific binding affinity to ACSS2. Treatment of BCBM cells with AD-5584 and AD-8007 leads to a significant reduction in colony formation, lipid storage, acetyl-CoA levels and cell survival in vitro. In an ex vivo brain-tumor slice model, treatment with AD-8007 and AD-5584 reduced pre-formed tumors and synergized with irradiation in blocking BCBM tumor growth. Treatment with AD-8007 reduced tumor burden and extended survival in vivo. This study identifies selective brain-penetrant ACSS2 inhibitors with efficacy towards breast cancer brain metastasis.

Efficient Generation of Conformer Ensembles Using Internal Coordinates and a Generative Directional Graph Convolution Neural Network

We present a neural-network-based high-throughput molecular conformer-generation algorithm. A chemical graph-convolutional network is trained to predict low-energy conformers in internal coordinate representation (bond lengths, bond, and torsion angles), starting from two-dimensional (2D) chemical topology. Generative neural network (NN) architecture performs denoising from torsion space, producing conformer ensembles with populations that are well correlated with torsion energy profiles. Short force-field-based energy minimization is applied to refine final conformers. All computation-intensive stages of the algorithm are GPU-optimized. The procedure (termed GINGER) is benchmarked on a commonly used test set of bioactive three-dimensional (3D) conformers from the PDB. We demonstrate highly competitive results in conformer recovery and throughput rates suitable for giga-scale compound library processing. A web server that allows interactive conformer ensemble generation by GINGER and their viewing is made freely available at https://www.molsoft.com/gingerdemo.html.

A Hybrid Approach Combining Shape-Based and Docking Methods to Identify Novel Potential P2X7 Antagonists from Natural Product Databases

P2X7 is an ATP-activated purinergic receptor implicated in pro-inflammatory responses. It is associated with the development of several diseases, including inflammatory and neurodegenerative conditions. Although several P2X7 receptor antagonists have recently been reported in the literature, none of them is approved for clinical use. However, the structure of the known antagonists can serve as a scaffold for discovering effective compounds in clinical therapy. This study aimed to propose an improved virtual screening methodology for the identification of novel potential P2X7 receptor antagonists from natural products through the combination of shape-based and docking approaches. First, a shape-based screening was performed based on the structure of JNJ-47965567, a P2X7 antagonist, using two natural product compound databases, MEGx (~5.8 × 103 compounds) and NATx (~32 × 103 compounds). Then, the compounds selected by the proposed shape-based model, with Shape-Tanimoto score values ranging between 0.624 and 0.799, were filtered for drug-like properties. Finally, the compounds that met the drug-like filter criteria were docked into the P2X7 allosteric binding site, using the docking programs GOLD and DockThor. The docking poses with the best score values were submitted to careful visual inspection of the P2X7 allosteric binding site. Based on our established visual inspection criteria, four compounds from the MEGx database and four from the NATx database were finally selected as potential P2X7 receptor antagonists. The selected compounds are structurally different from known P2X7 antagonists, have drug-like properties, and are predicted to interact with key P2X7 allosteric binding pocket residues, including F88, F92, F95, F103, M105, F108, Y295, Y298, and I310. Therefore, the combination of shape-based screening and docking approaches proposed in our study has proven useful in selecting potential novel P2X7 antagonist candidates from natural-product-derived compounds databases. This approach could also be useful for selecting potential inhibitors/antagonists of other receptors and/or biological targets.

A virtual library of small molecules mimicking dipeptides

Virtual combinatorial libraries are prevalent in drug discovery due to improvements in the prediction of synthetic reactions that can be performed. This has gone hand in hand with the development of virtual screening capabilities to effectively screen the large chemical spaces spanned by exhaustive enumeration of reaction products. In this study, we generated a small-molecule dipeptide mimic library to target proteins binding small peptides. The library was created based on the general idea of peptide synthesis, that is, amino acid mimics were reacted in silico to form the dipeptide mimics, yielding 2,036,819 unique compounds. After docking calculations, two compounds from the library were synthesized and tested against WD repeat-containing protein 5 (WDR5) and histamine receptors H1-H4 to evaluate whether these molecules are viable in assays. The compounds showed the highest potency at the histamine H3 receptor, with Ki values in the two-digit micromolar range.

Identification and targeting of metastatic biomarkers for hepatocellular carcinoma therapeutics using small molecules library of curcumin analogues

The understanding of the molecular basis of complex diseases like hepatocellular carcinoma (HCC) needs large datasets of multiple genes and proteins involved in different phenomenon of its development. This study focuses on the molecular basis of HCC and the development of therapeutic strategies. We analyzed a dataset of 5475 genes (Homo sapiens) involved in HCC hallmarks, involving comprehensive data on multiple genes and frequently mutated genes. As HCC is characterized by metastasis, angiogenesis, and oxidative stress, exploration of genes associated with them has been targeted. Through gene ontology, functional characterization, and pathway enrichment analysis, we identified target proteins such as Lysyl oxidase, Survivin, Cofilin, and Cathepsin B. A library of curcumin analogs was used to target these proteins. Tetrahrydrocurcumin showed promising binding affinities for all four proteins, suggesting its potential as an inhibitor against these proteins for HCC therapy.

On the relevance of query definition in the performance of 3D ligand-based virtual screening

Ligand-based virtual screening (LBVS) methods are widely used to explore the vast chemical space in the search of novel compounds resorting to a variety of properties encoded in 1D, 2D or 3D descriptors. The success of 3D-LBVS is affected by the overlay of molecular pairs, thus making selection of the template compound, search of accessible conformational space and choice of the query conformation to be potential factors that modulate the successful retrieval of actives. This study examines the impact of adopting different choices for the query conformation of the template, paying also attention to the influence exerted by the structural similarity between templates and actives. The analysis is performed using PharmScreen, a 3D LBVS tool that relies on similarity measurements of the hydrophobic/philic pattern of molecules, and Phase Shape, which is based on the alignment of atom triplets followed by refinement of the volume overlap. The study is performed for the original DUD-E+ database and a Morgan Fingerprint filtered version (denoted DUD-E+-Diverse; available in https://github.com/Pharmacelera/Query-models-to-3DLBVS ), which was prepared to minimize the 2D resemblance between template and actives. Although in most cases the query conformation exhibits a mild influence on the overall performance, a critical analysis is made to disclose factors, such as the content of structural features between template and actives and the induction of conformational strain in the template, that underlie the drastic impact of the query definition in the recovery of actives for certain targets. The findings of this research also provide valuable guidance for assisting the selection of the query definition in 3D LBVS campaigns.

Chemoinformatic approaches for navigating large chemical spaces

INTRODUCTION

Large chemical spaces (CSs) include traditional large compound collections, combinatorial libraries covering billions to trillions of molecules, DNA-encoded chemical libraries comprising complete combinatorial CSs in a single mixture, and virtual CSs explored by generative models. The diverse nature of these types of CSs require different chemoinformatic approaches for navigation.

AREAS COVERED

An overview of different types of large CSs is provided. Molecular representations and similarity metrics suitable for large CS exploration are discussed. A summary of navigation of CSs in generative models is provided. Methods for characterizing and comparing CSs are discussed.

EXPERT OPINION

The size of large CSs might restrict navigation to specialized algorithms and limit it to considering neighborhoods of structurally similar molecules. Efficient navigation of large CSs not only requires methods that scale with size but also requires smart approaches that focus on better but not necessarily larger molecule selections. Deep generative models aim to provide such approaches by implicitly learning features relevant for targeted biological properties. It is unclear whether these models can fulfill this ideal as validation is difficult as long as the covered CSs remain mainly virtual without experimental verification.

Pocket Crafter: a 3D generative modeling based workflow for the rapid generation of hit molecules in drug discovery

We present a user-friendly molecular generative pipeline called Pocket Crafter, specifically designed to facilitate hit finding activity in the drug discovery process. This workflow utilized a three-dimensional (3D) generative modeling method Pocket2Mol, for the de novo design of molecules in spatial perspective for the targeted protein structures, followed by filters for chemical-physical properties and drug-likeness, structure-activity relationship analysis, and clustering to generate top virtual hit scaffolds. In our WDR5 case study, we acquired a focused set of 2029 compounds after a targeted searching within Novartis archived library based on the virtual scaffolds. Subsequently, we experimentally profiled these compounds, resulting in a novel chemical scaffold series that demonstrated activity in biochemical and biophysical assays. Pocket Crafter successfully prototyped an effective end-to-end 3D generative chemistry-based workflow for the exploration of new chemical scaffolds, which represents a promising approach in early drug discovery for hit identification.

Targeted sampling of natural product space to identify bioactive natural product-like polyketide macrolides

Polyketide or polyketide-like macrolides (pMLs) continue to serve as a source of inspiration for drug discovery. However, their inherent structural and stereochemical complexity challenges efforts to explore related regions of chemical space more broadly. Here, we report a strategy termed the Targeted Sampling of Natural Product space (TSNaP) that is designed to identify and assess regions of chemical space bounded by this important class of molecules. Using TSNaP, a family of tetrahydrofuran-containing pMLs are computationally assembled from pML inspired building blocks to provide a large collection of natural product-like virtual pMLs. By scoring functional group and volumetric overlap against their natural counterparts, a collection of compounds are prioritized for targeted synthesis. Using a modular and stereoselective synthetic approach, a library of polyketide-like macrolides are prepared to sample these unpopulated regions of pML chemical space. Validation of this TSNaP approach by screening this library against a panel of whole-cell biological assays, reveals hit rates exceeding those typically encountered in small molecule libraries. This study suggests that the TSNaP approach may be more broadly useful for the design of improved chemical libraries for drug discovery.

Sample efficient reinforcement learning with active learning for molecular design

Reinforcement learning (RL) is a powerful and flexible paradigm for searching for solutions in high-dimensional action spaces. However, bridging the gap between playing computer games with thousands of simulated episodes and solving real scientific problems with complex and involved environments (up to actual laboratory experiments) requires improvements in terms of sample efficiency to make the most of expensive information. The discovery of new drugs is a major commercial application of RL, motivated by the very large nature of the chemical space and the need to perform multiparameter optimization (MPO) across different properties. In silico methods, such as virtual library screening (VS) and de novo molecular generation with RL, show great promise in accelerating this search. However, incorporation of increasingly complex computational models in these workflows requires increasing sample efficiency. Here, we introduce an active learning system linked with an RL model (RL-AL) for molecular design, which aims to improve the sample-efficiency of the optimization process. We identity and characterize unique challenges combining RL and AL, investigate the interplay between the systems, and develop a novel AL approach to solve the MPO problem. Our approach greatly expedites the search for novel solutions relative to baseline-RL for simple ligand- and structure-based oracle functions, with a 5-66-fold increase in hits generated for a fixed oracle budget and a 4-64-fold reduction in computational time to find a specific number of hits. Furthermore, compounds discovered through RL-AL display substantial enrichment of a multi-parameter scoring objective, indicating superior efficacy in curating high-scoring compounds, without a reduction in output diversity. This significant acceleration improves the feasibility of oracle functions that have largely been overlooked in RL due to high computational costs, for example free energy perturbation methods, and in principle is applicable to any RL domain.

Benzbromarone Induces Targeted Degradation of HSP47 Protein and Improves Hypertrophic Scar Formation

Fibrotic diseases are characterized by the abnormal accumulation of collagen in the extracellular matrix, leading to the functional impairment of various organs. In the skin, excessive collagen deposition manifests as hypertrophic scars and keloids, placing a substantial burden on patients and the healthcare system worldwide. HSP47 is essential for proper collagen assembly and contributes to fibrosis. However, identifying clinically applicable HSP47 inhibitors has been a major pharmaceutical challenge. In this study, we identified benzbromarone (BBR) as an HSP47 inhibitor for hypertrophic scarring treatment. BBR inhibited collagen production and secretion in fibroblasts from patients with keloid by binding to HSP47 and inhibiting the interaction between HSP47 and collagen. Interestingly, BBR not only inhibits HSP47 but also acts as a molecular glue degrader that promotes its proteasome-dependent degradation. Through these molecular mechanisms, BBR effectively reduced hypertrophic scarring in mini pigs and rats with burns and/or excisional skin damage. Thus, these findings suggest that BBR can be used to clinically treat hypertrophic scars and, more generally, fibrotic diseases.

Quantitative live cell imaging of a tauopathy model enables the identification of a polypharmacological drug candidate that restores physiological microtubule interaction

Tauopathies such as Alzheimer's disease are characterized by aggregation and increased phosphorylation of the microtubule-associated protein tau. Tau's pathological changes are closely linked to neurodegeneration, making tau a prime candidate for intervention. We developed an approach to monitor pathological changes of aggregation-prone human tau in living neurons. We identified 2-phenyloxazole (PHOX) derivatives as putative polypharmacological small molecules that interact with tau and modulate tau kinases. We found that PHOX15 inhibits tau aggregation, restores tau's physiological microtubule interaction, and reduces tau phosphorylation at disease-relevant sites. Molecular dynamics simulations highlight cryptic channel-like pockets crossing tau protofilaments and suggest that PHOX15 binding reduces the protofilament's ability to adopt a PHF-like conformation by modifying a key glycine triad. Our data demonstrate that live-cell imaging of a tauopathy model enables screening of compounds that modulate tau-microtubule interaction and allows identification of a promising polypharmacological drug candidate that simultaneously inhibits tau aggregation and reduces tau phosphorylation.

Reinvent 4: Modern AI-driven generative molecule design

REINVENT 4 is a modern open-source generative AI framework for the design of small molecules. The software utilizes recurrent neural networks and transformer architectures to drive molecule generation. These generators are seamlessly embedded within the general machine learning optimization algorithms, transfer learning, reinforcement learning and curriculum learning. REINVENT 4 enables and facilitates de novo design, R-group replacement, library design, linker design, scaffold hopping and molecule optimization. This contribution gives an overview of the software and describes its design. Algorithms and their applications are discussed in detail. REINVENT 4 is a command line tool which reads a user configuration in either TOML or JSON format. The aim of this release is to provide reference implementations for some of the most common algorithms in AI based molecule generation. An additional goal with the release is to create a framework for education and future innovation in AI based molecular design. The software is available from https://github.com/MolecularAI/REINVENT4 and released under the permissive Apache 2.0 license. Scientific contribution. The software provides an open-source reference implementation for generative molecular design where the software is also being used in production to support in-house drug discovery projects. The publication of the most common machine learning algorithms in one code and full documentation thereof will increase transparency of AI and foster innovation, collaboration and education.

Selective Bias Virtual Screening for Discovery of Promising Antimalarial Candidates targeting Plasmodium N-Myristoyltransferase

Malaria remains a significant public health challenge, with Plasmodium vivax being the species responsible for the most prevalent form of the disease. Given the limited therapeutic options available, the search for new antimalarials against P. vivax is urgent. This study aims to identify new inhibitors for P. vivax N-myristoyltransferase (PvNMT), an essential drug target against malaria. Through a validated virtual screening campaign, we prioritized 23 candidates for further testing. In the yeast NMT system, seven compounds exhibit a potential inhibitor phenotype. In vitro antimalarial phenotypic assays confirmed the activity of four candidates while demonstrating an absence of cytotoxicity. Enzymatic assays reveal LabMol-394 as the most promising inhibitor, displaying selectivity against the parasite and a strong correlation within the yeast system. Furthermore, molecular dynamics simulations shed some light into its binding mode. This study constitutes a substantial contribution to the exploration of a selective quinoline scaffold and provides valuable insights into the development of new antimalarial candidates.

Molecular Gas-Phase Conformational Ensembles

Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and for identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field-based approaches. To validate these methods, we propose the use of collisional cross-section (CCS) values obtained from ion mobility-mass spectrometry studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules and can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values, expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformational generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows for predicting the structures of unknown compounds with high precision.

GRELinker: A Graph-Based Generative Model for Molecular Linker Design with Reinforcement and Curriculum Learning

Fragment-based drug discovery (FBDD) is widely used in drug design. One useful strategy in FBDD is designing linkers for linking fragments to optimize their molecular properties. In the current study, we present a novel generative fragment linking model, GRELinker, which utilizes a gated-graph neural network combined with reinforcement and curriculum learning to generate molecules with desirable attributes. The model has been shown to be efficient in multiple tasks, including controlling log P, optimizing synthesizability or predicted bioactivity of compounds, and generating molecules with high 3D similarity but low 2D similarity to the lead compound. Specifically, our model outperforms the previously reported reinforcement learning (RL) built-in method DRlinker on these benchmark tasks. Moreover, GRELinker has been successfully used in an actual FBDD case to generate optimized molecules with enhanced affinities by employing the docking score as the scoring function in RL. Besides, the implementation of curriculum learning in our framework enables the generation of structurally complex linkers more efficiently. These results demonstrate the benefits and feasibility of GRELinker in linker design for molecular optimization and drug discovery.

Synthesis and insecticidal activity of N-(5-phenylpyrazin-2-yl)-benzamide derivatives: Elucidation of mode of action on chitin biosynthesis through symptomology and genetic studies

Among the six-membered heterocycles, the pyrazine ring is poorly explored in crop protection and does not feature in any product listed in the current IRAC MoA classification. In an effort to identify new leads for internal research, we synthesized a series of N-(5-phenylpyrazin-2-yl)-benzamide derivatives and evaluated them for their insecticidal activity. N-(5-phenylpyrazin-2-yl)-benzamide derivatives 3 were prepared using an automated two-step synthesis protocol. These compounds were tested for their initial biological activity against a wide range of sucking and chewing insect pests and found to be active against lepidopterans only. More detailed experiments, including symptomology studies on the diamondback moth, Plutella xylostella (L.) and the Egyptian cotton leafworm, Spodoptera littoralis (Boisduval) showed that analog 3q causes severe abnormalities in the lepidopteran cuticle leading to larval mortality. Compound 3q shows strong potency against both P. xylostella and S. littoralis, whereas analog 3i shows better potency against S. littoralis causing also impaired cuticular structure and death of the larvae. Additionally, P. xylostella genetic studies showed that compound 3q resistance is linked to Chitin Synthase 1. Our studies show that N-(5-phenylpyrazin-2-yl)-benzamide derivatives 3, and in particular analogs 3i and 3q, act as insect growth modulator insecticides. Conformational similarities with lufenuron are discussed.

Docking and other computing tools in drug design against SARS-CoV-2

The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.

Design, Synthesis, and Evaluation of New 1H-Benzo[d]imidazole Based PqsR Inhibitors as Adjuvant Therapy for Pseudomonas aeruginosa Infections

Pseudomonas aeruginosa is one of the top priority pathogens that requires immediate attention according to the World Health Organisation (WHO). Due to the alarming shortage of novel antimicrobials, targeting quorum sensing (QS), a bacterial cell to cell signaling system controlling virulence, has emerged as a promising approach as an antibiotic adjuvant therapy. Interference with the pqs system, one of three QS systems in P. aeruginosa, results in reduction of bacterial virulence gene expression and biofilm maturation. Herein, we report a hit to lead process to fine-tune the potency of our previously reported inhibitor 1 (IC50 3.2 μM in P. aeruginosa PAO1-L), which led to the discovery of 2-(4-(3-((6-chloro-1-isopropyl-1H-benzo[d]imidazol-2-yl)amino)-2-hydroxypropoxy)phenyl)acetonitrile (6f) as a potent PqsR antagonist. Compound 6f inhibited the PqsR-controlled PpqsA-lux transcriptional reporter fusion in P. aeruginosa at low submicromolar concentrations. Moreover, 6f showed improved efficacy against P. aeruginosa CF isolates with significant inhibition of pyocyanin, 2-alkyl-4(1H)-quinolones production.

Aminopyrimidine Derivatives as Multiflavivirus Antiviral Compounds Identified from a Consensus Virtual Screening Approach

Around three billion people are at risk of infection by the dengue virus (DENV) and potentially other flaviviruses. Worldwide outbreaks of DENV, Zika virus (ZIKV), and yellow fever virus (YFV), the lack of antiviral drugs, and limitations on vaccine usage emphasize the need for novel antiviral research. Here, we propose a consensus virtual screening approach to discover potential protease inhibitors (NS3pro) against different flavivirus. We employed an in silico combination of a hologram quantitative structure-activity relationship (HQSAR) model and molecular docking on characterized binding sites followed by molecular dynamics (MD) simulations, which filtered a data set of 7.6 million compounds to 2,775 hits. Lastly, docking and MD simulations selected six final potential NS3pro inhibitors with stable interactions along the simulations. Five compounds had their antiviral activity confirmed against ZIKV, YFV, DENV-2, and DENV-3 (ranging from 4.21 ± 0.14 to 37.51 ± 0.8 μM), displaying aggregator characteristics for enzymatic inhibition against ZIKV NS3pro (ranging from 28 ± 7 to 70 ± 7 μM). Taken together, the compounds identified in this approach may contribute to the design of promising candidates to treat different flavivirus infections.

Phytochemical analysis and antifungal potential of two Launaea mucronata (Forssk.) Muschl and Launaea nudicaulis (L.) Hook.fil. wildly growing in Anbar province, Iraq

ETHNOPHARMACOLOGICAL RELEVANCE

Plant fungi are a serious problem in agriculture. Even though synthetic fungicides are an efficient control method, several negative side effects emerge from their extensive use, such as health problems, environmental pollution, and the emergence of resistant microorganisms. Thus, it is becoming more and more urgent to search for alternative control methods.

AIM OF THE STUDY

The aim of our study was to analyze phytochemical composition and antifungal potential of Launaea mucronata (Forssk.) Muschl. and Launaea nudicaulis (L.) Hook. fil. wildly growing in Anbar province, Iraq. In addition, molecular analysis was used to identify the plants species and molecular docking analysis was investigated between the major phytochemicals present in these plants and three selected fungal proteins, in order to assess the antifungal activity of the selected biochemicals against these proteins.

MATERIALS AND METHODS

Molecular analysis was performed using ITS sequencing protocol. The phytochemical analysis was done using GC-MS technique, while molecular docking analysis was performed by FRED application between selected compounds from each plant and three enzymes: 17β-hydroxysteroid dehydrogenase, endochitinase, and 14-α-demethylase. Finally, the antifungal activity was assessed by measuring inhibition percentage of Fusarium solani and Macrophomina phaseolina growth treated with ethanomethanolic extract of each plant.

RESULTS

Molecular analysis identified the selected plants as L. mucronata and L. nudicaulis, with an ITS region of 600 bp. Phytochemical analysis of Launaea spp. reported the presence of 35 compounds in each ethanomethanolic extract, belonging to different classes. L. mucronata was mainly formed of lupeol (9.33%), whereas L. nudicaulis extract was dominated by the heterocyclic compound 4-(3-methoxyphenoxy)-1,2,5-oxadiazol-3-amine (20.2%). Furthermore, molecular docking analysis showed that 4H-pyran-4-one,2,3-dihydro-3,5-dihydroxy-6-methyl from L. mucronata and gulonic acid Ƴ-lactone from L. nudicaulis possessed the highest affinity score to 17-β-hydroxysteroid dehydrogenase (-4.584 and -7.811 kcal/mol, respectively). Sucrose from L. mucronata and glutaric acid, di(3,4-difluorobenzyl) ester from L. nudicaulis gave the highest affinity to endochitinase (-7.979 and - 8.446 kcal/mol, respectively). In addition, sterol 14-α-demethylase was affinitive to sucrose from L. mucronata and glutaric acid, di(3,4-difluorobenzyl) ester from L. nudicaulis via energetic score of -10.002 and -9.582 kcal/mol, respectively. Both extracts exhibited antifungal activity against F. solani and M. phaseolina in a dose-dependent manner.

CONCLUSIONS

This study confirms the antifungal potential of both Launaea spp. explained by the presence of phytochemicals with antimicrobial properties. These compounds have potential to be used as new drugs to treat infectious diseases caused by pathogens. Consequently, plants from Launaea genus could be a raw material for many studies such as therapeutic, taxonomical, drug modelling, and antifungal agent.

Automated Virtual Screening

Computational methods in modern drug discovery have become ubiquitous, with methods that cover most of the discovery stages: from hit finding and lead identification to lead optimization. The overall aim of these computational methods is to obtain a more efficient discovery process, by reducing the number of "wet" experiments required to produce therapeutics that have higher probability of succeeding in clinical development and subsequently benefitting end patients by developing highly effective therapeutics having minimal side effects. Virtual Screening is usually applied at the early stage of drug discovery, looking to find chemical matter having desired properties, such as molecular shape, electrostatics, and pharmacophores at desired three-dimensional positions. The aim of this stage is to search in a wide chemical space, including chemistry available from commercial suppliers and virtual databases of predicted reaction products, to identify molecules that would exert a particular biochemical response. This initial stage of the discovery process is very important since the subsequent stages will use the initial chemical motifs that have been found at the hit finding stage, and therefore the most suitable the compound is found, the more likely it is that subsequent stages will be successful and less time and resource consuming. This chapter provides a summary of various Virtual Screening methods, including shape match and molecular docking, and these methods are used in a Virtual Screening workflow that is provided as an example which is described to be run automatically in cloud resources. This automatic in-depth exploration of the chemical space using validated Virtual Screening methods can lead to a more streamlined and efficient discovery process, aiming to deliver chemical matter of high quality and maximizing the required biological effects while minimizing adverse effects. Surely, Virtual Screening pipelines of this nature will continue to play a central role in producing much needed therapeutics for the health challenges of the future.

Discovery of novel brain permeable human ACSS2 inhibitors for blocking breast cancer brain metastatic growth

Breast-cancer brain metastasis (BCBM) poses a significant clinical challenge, resulting in an end-stage diagnosis and hindered by limited therapeutic options. The blood-brain barrier (BBB) acts as an anatomical and physiological hurdle for therapeutic compounds, restricting the effective delivery of therapies to the brain. In order to grow and survive in a nutrient-poor environment, tumors in the brain must adapt to their metabolic needs, becoming highly dependent on acetate. These tumors rely on the conversion of acetate to acetyl-CoA by the enzyme Acetyl-CoA synthetase 2 (ACSS2), a key metabolic enzyme involved in regulating fatty acid synthesis and protein acetylation in tumor cells. ACSS2 has emerged as a crucial enzyme required for the growth of tumors in the brain. Here, we utilized a computational pipeline, combining pharmacophore-based shape screen methodology with ADME property predictions to identify novel brain-permeable ACSS2 inhibitors. From a small molecule library, this approach identified 30 potential ACSS2 binders, from which two candidates, AD-5584 and AD-8007, were validated for their binding affinity, predicted metabolic stability, and, notably, their ability to traverse the BBB. We show that treatment of BCBM cells, MDA-MB-231BR, with AD-5584 and AD-8007 leads to a significant reduction in lipid storage, reduction in colony formation, and increase in cell death in vitro . Utilizing an ex vivo orthotopic brain-slice tumor model, we show that treatment with AD-8007 and AD-5584 significantly reduces tumor size and synergizes with radiation in blocking BCBM tumor growth ex vivo. Importantly, we show that following intraperitoneal injections with AD-5584 and AD-8007, we can detect these compounds in the brain, confirming their BBB permeability. Thus, we have identified and validated novel ACSS2 inhibitor candidates for further drug development and optimization as agents for treating patients with breast cancer brain metastasis.

Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations

Identifying bioactive conformations of small molecules is an essential process for virtual screening applications relying on three-dimensional structure such as molecular docking. For most small molecules, conformer generators retrieve at least one bioactive-like conformation, with an atomic root-mean-square deviation (ARMSD) lower than 1 Å, among the set of low-energy conformers generated. However, there is currently no general method to prioritise these likely target-bound conformations in the ensemble. In this work, we trained atomistic neural networks (AtNNs) on 3D information of generated conformers of a curated subset of PDBbind ligands to predict the ARMSD to their closest bioactive conformation, and evaluated the early enrichment of bioactive-like conformations when ranking conformers by AtNN prediction. AtNN ranking was compared with bioactivity-unaware baselines such as ascending Sage force field energy ranking, and a slower bioactivity-based baseline ranking by ascending Torsion Fingerprint Deviation to the Maximum Common Substructure to the most similar molecule in the training set (TFD2SimRefMCS). On test sets from random ligand splits of PDBbind, ranking conformers using ComENet, the AtNN encoding the most 3D information, leads to early enrichment of bioactive-like conformations with a median BEDROC of 0.29 ± 0.02, outperforming the best bioactivity-unaware Sage energy ranking baseline (median BEDROC of 0.18 ± 0.02), and performing on a par with the bioactivity-based TFD2SimRefMCS baseline (median BEDROC of 0.31 ± 0.02). The improved performance of the AtNN and TFD2SimRefMCS baseline is mostly observed on test set ligands that bind proteins similar to proteins observed in the training set. On a more challenging subset of flexible molecules, the bioactivity-unaware baselines showed median BEDROCs up to 0.02, while AtNNs and TFD2SimRefMCS showed median BEDROCs between 0.09 and 0.13. When performing rigid ligand re-docking of PDBbind ligands with GOLD using the 1% top-ranked conformers, ComENet ranked conformers showed a higher successful docking rate than bioactivity-unaware baselines, with a rate of 0.48 ± 0.02 compared to CSD probability baseline with a rate of 0.39 ± 0.02. Similarly, on a pharmacophore searching experiment, selecting the 20% top-ranked conformers ranked by ComENet showed higher hit rate compared to baselines. Hence, the approach presented here uses AtNNs successfully to focus conformer ensembles towards bioactive-like conformations, representing an opportunity to reduce computational expense in virtual screening applications on known targets that require input conformations.

Combined Structure- and Ligand-Based Approach for the Identification of Inhibitors of AcrAB-TolC in Escherichia coli

The inhibition of efflux pumps is a promising approach to combating multidrug-resistant bacteria. We have developed a combined structure- and ligand-based model, using OpenEye software, for the identification of inhibitors of AcrB, the inner membrane protein component of the AcrAB-TolC efflux pump in Escherichia coli. From a database of 1391 FDA-approved drugs, 23 compounds were selected to test for efflux inhibition in E. coli. Seven compounds, including ivacaftor (25), butenafine (19), naftifine (27), pimozide (30), thioridazine (35), trifluoperazine (37), and meloxicam (26), enhanced the activity of at least one antimicrobial substrate and inhibited the efflux pump-mediated removal of the substrate Nile Red from cells. Ivacaftor (25) inhibited efflux dose dependently, had no effect on an E. coli strain with genomic deletion of the gene encoding AcrB, and did not damage the bacterial outer membrane. In the presence of a sub-minimum inhibitory concentration (MIC) of the outer membrane permeabilizer colistin, ivacaftor at 1 μg/mL reduced the MICs of erythromycin and minocycline by 4- to 8-fold. The identification of seven potential AcrB inhibitors shows the merits of a combined structure- and ligand-based approach to virtual screening.

Hydrolytic Activity of Mitochondrial F1FO-ATP Synthase as a Target for Myocardial Ischemia-Reperfusion Injury: Discovery and In Vitro and In Vivo Evaluation of Novel Inhibitors

F1FO-ATP synthase is the mitochondrial complex responsible for ATP production. During myocardial ischemia, it reverses its activity, hydrolyzing ATP and leading to energetic deficit and cardiac injury. We aimed to discover novel inhibitors of ATP hydrolysis, accessing the druggability of the target within ischemia(I)/reperfusion(R) injury. New molecular scaffolds were revealed using ligand-based virtual screening methods. Fifty-five compounds were tested on isolated murine heart mitochondria and H9c2 cells for their inhibitory activity. A pyrazolo[3,4-c]pyridine hit structure was identified and optimized in a hit-to-lead process synthesizing nine novel derivatives. Three derivatives significantly inhibited ATP hydrolysis in vitro, while in vivo, they reduced myocardial infarct size (IS). The novel compound 31 was the most effective in reducing IS, validating that inhibition of F1FO-ATP hydrolytic activity can serve as a target for cardioprotection during ischemia. Further examination of signaling pathways revealed that the cardioprotection mechanism is related to the increased ATP content in the ischemic myocardium and increased phosphorylation of PKA and phospholamban, leading to the reduction of apoptosis.

DataPype: A Fully Automated Unified Software Platform for Computer-Aided Drug Design

With the advent of computer-aided drug design (CADD), traditional physical testing of thousands of molecules has now been replaced by target-focused drug discovery, where potentially bioactive molecules are predicted by computer software before their physical synthesis. However, despite being a significant breakthrough, CADD still faces various limitations and challenges. The increasing availability of data on small molecules has created a need to streamline the sourcing of data from different databases and automate the processing and cleaning of data into a form that can be used by multiple CADD software applications. Several standalone software packages are available to aid the drug designer, each with its own specific application, requiring specialized knowledge and expertise for optimal use. These applications require their own input and output files, making it a challenge for nonexpert users or multidisciplinary discovery teams. Here, we have developed a new software platform called DataPype, which wraps around these different software packages. It provides a unified automated workflow to search for hit compounds using specialist software. Additionally, multiple virtual screening packages can be used in the one workflow, and if different ways of looking at potential hit compounds all predict the same set of molecules, we have higher confidence that we should make or purchase and test the molecules. Importantly, DataPype can run on computer servers, speeding up the virtual screening for new compounds. Combining access to multiple CADD tools within one interface will enhance the early stage of drug discovery, increase usability, and enable the use of parallel computing.

Identifying inhibitors of Trypanosoma cruzi nucleoside diphosphate kinase 1 as potential repurposed drugs for Chagas' disease

Trypanosoma cruzi is the causative agent of Chagas' disease, an endemic and neglected disease. The treatment is limited to only two drugs, benznidazole (BZL) and nifurtimox (NFX), introduced more than fifty years ago and no new advances have been made since then. Nucleoside diphosphate kinases (NDPK) are key metabolic enzymes which have gained interest as drug targets of pathogen organisms. Taking advantage of the computer-assisted drug repurposing approaches, in the present work we initiate a search of potential T. cruzi nucleoside diphosphate kinase 1 (TcNDPK1) inhibitors over an ∼ 12,000 compound structures database to find drugs targeted to this enzyme with trypanocidal activity. Four medicines were selected and evaluated in vitro, ketorolac (KET, an anti-inflamatory), dutasteride (DUT, used to treat benign prostatic hyperplasia), nebivolol and telmisartan (NEB and TEL, used to treat high blood pressure). The four compounds were weak inhibitors and presented different trypanocidal effect on epimastigotes, trypomastigotes and intracellular stages. NEB and TEL were the most active drugs with increased effect on intracellular stages, (IC50 = 2.25 µM and 13.21 µM respectively), and selectivity indexes of 13.01 and 8.59 respectively, showing comparable effect to BZL, the first line drug for Chagas' disease treatment. In addition, both presented positive interactions when combined with BZL. Finally, transgenic epimastigotes with increased expression of TcNDPK1 were more resistant to TEL and NEB, suggesting that TcNDPK1 is at least one of the molecular targets. In view of the results, NEB and TEL could be repurposed medicines for Chagas' disease therapy.

3D based generative PROTAC linker design with reinforcement learning

Proteolysis targeting chimera (PROTAC), has emerged as an effective modality to selectively degrade disease-related proteins by harnessing the ubiquitin-proteasome system. Due to PROTACs' hetero-bifunctional characteristics, in which a linker joins a warhead binding to a protein of interest (POI), conferring specificity and a E3-ligand binding to an E3 ubiquitin ligase, this could trigger the ubiquitination and transportation of POI to the proteasome, followed by degradation. The rational PROTAC linker design is challenging due to its relatively large molecular weight and the complexity of maintaining the binding mode of warhead and E3-ligand in the binding pockets of counterpart. Conventional linker generation method can only generate linkers in either 1D SMILES or 2D graph, without taking into account the information of ternary structures. Here we propose a novel 3D linker generative model PROTAC-INVENT which can not only generate SMILES of PROTAC but also its 3D putative binding conformation coupled with the target protein and the E3 ligase. The model is trained jointly with the RL approach to bias the generation of PROTAC structures toward pre-defined 2D and 3D based properties. Examples were provided to demonstrate the utility of the model for generating reasonable 3D conformation of PROTACs. On the other hand, our results show that the associated workflow for 3D PROTAC conformation generation can also be used as an efficient docking protocol for PROTACs.

Identification of potential inhibitors for N-myristoyltransferase (NMT) protein of Plasmodium vivax

Malaria is a neglected parasitic infection of global importance. It is mainly present in tropical countries and caused by a protozoa that belongs to the genus Plasmodium. The disease vectors are female Anopheles mosquitoes infected with the Plasmodium spp. According to the World Health Organization (WHO), there were 241 million malaria cases worldwide in 2020 and approximately 627 thousand malaria deaths in the same year. The increasing resistance to treatment has been a major problem since the beginning of the 21st century. New studies have been conducted to find possible drugs that can be used for the eradication of the disease. In this scenario, a protein named N-myristoyltransferase (NMT) has been studied as a potential drug target. NMT has an important role on the myristoylation of proteins and binds to the plasma membrane, contributing to the stabilization of protein-protein interactions. Thus, inhibition of NMT can lead to death of the parasite cell. Therefore, in order to predict and detect potential inhibitors against Plasmodium NMT, Computer-Aided Drug Design techniques were used in this research that involve virtual screening, molecular docking, and molecular dynamics. Three potential compounds similar to a benzofuran inhibitor were identified as stable PvNMT ligands. These compounds (EXP90, ZBC205 and ZDD968) originate from three different sources, respectively: a commercial library, a natural product library, and the FDA approved drugs dataset. These compounds may be further tested in in vitro and in vivo inhibition tests against Plasmodium vivax NMT.Communicated by Ramaswamy H. Sarma.

Building Block-Based Binding Predictions for DNA-Encoded Libraries

DNA-encoded libraries (DELs) provide the means to make and screen millions of diverse compounds against a target of interest in a single experiment. However, despite producing large volumes of binding data at a relatively low cost, the DEL selection process is susceptible to noise, necessitating computational follow-up to increase signal-to-noise ratios. In this work, we present a set of informatics tools to employ data from prior DEL screen(s) to gain information about which building blocks are most likely to be productive when designing new DELs for the same target. We demonstrate that similar building blocks have similar probabilities of forming compounds that bind. We then build a model from the inference that the combined behavior of individual building blocks is predictive of whether an overall compound binds. We illustrate our approach on a set of three-cycle OpenDEL libraries screened against soluble epoxide hydrolase (sEH) and report performance of more than an order of magnitude greater than random guessing on a holdout set, demonstrating that our model can serve as a baseline for comparison against other machine learning models on DEL data. Lastly, we provide a discussion on how we believe this informatics workflow could be applied to benefit researchers in their specific DEL campaigns.

Application of Quantitative Structure-Activity Relationships in the Prediction of New Compounds with Anti-Leukemic Activity

Leukemia invades the bone marrow progressively and, through unknown mechanisms, outcompetes healthy hematopoiesis. Protein arginine methyltransferases 1 (PRMT1) are found in prokaryotes and eukaryotes cells. They are necessary for a number of biological processes and have been linked to several human diseases, including cancer. Small compounds that target PRMT1 have a significant impact on both functional research and clinical disease treatment. In fact, numerous PRMT1 inhibitors targeting the S-adenosyl-L-methionine binding region have been studied. Through topographical descriptors, quantitative structure-activity relationships (QSAR) were developed in order to identify the most effective PRMT1 inhibitors among 17 compounds. The model built using linear discriminant analysis allows us to accurately classify over 90% of the investigated active substances. Antileukemic activity is predicted using a multilinear regression analysis, and it can account for more than 56% of the variation. Both analyses are validated using an internal "leave some out" test. The developed model could be utilized in future preclinical experiments with novel drugs.

Application of Quantitative Structure-Activity Relationships in the Prediction of New Compounds with Anti-Leukemic Activity

Leukemia invades the bone marrow progressively and, through unknown mechanisms, outcompetes healthy hematopoiesis. Protein arginine methyltransferases 1 (PRMT1) are found in prokaryotes and eukaryotes cells. They are necessary for a number of biological processes and have been linked to several human diseases, including cancer. Small compounds that target PRMT1 have a significant impact on both functional research and clinical disease treatment. In fact, numerous PRMT1 inhibitors targeting the S-adenosyl-L-methionine binding region have been studied. Through topographical descriptors, quantitative structure-activity relationships (QSAR) were developed in order to identify the most effective PRMT1 inhibitors among 17 compounds. The model built using linear discriminant analysis allows us to accurately classify over 90% of the investigated active substances. Antileukemic activity is predicted using a multilinear regression analysis, and it can account for more than 56% of the variation. Both analyses are validated using an internal “leave some out” test. The developed model could be utilized in future preclinical experiments with novel drugs.

Discovery of New Binders for DCAF1, an Emerging Ligase Target in the Targeted Protein Degradation Field

In this study, we describe the rapid identification of potent binders for the WD40 repeat domain (WDR) of DCAF1. This was achieved by two rounds of iterative focused screening of a small set of compounds selected on the basis of internal WDR domain knowledge followed by hit expansion. Subsequent structure-based design led to nanomolar potency binders with a clear exit vector enabling DCAF1-based bifunctional degrader exploration.

Identification of N-Acyl Hydrazones as New Non-Zinc-Binding MMP-13 Inhibitors by Structure-Based Virtual Screening Studies and Chemical Optimization

Matrix metalloproteinase 13 plays a central role in osteoarthritis (OA), as its overexpression induces an excessive breakdown of collagen that results in an imbalance between collagen synthesis and degradation in the joint, leading to progressive articular cartilage degradation. Therefore, MMP-13 has been proposed as a key therapeutic target for OA. Here we have developed a virtual screening workflow aimed at identifying selective non-zinc-binding MMP-13 inhibitors by targeting the deep S1' pocket of MMP-13. Three ligands were found to inhibit MMP-13 in the µM range, and one of these showed selectivity over other MMPs. A structure-based analysis guided the chemical optimization of the hit compound, leading to the obtaining of a new N-acyl hydrazone-based derivative with improved inhibitory activity and selectivity for the target enzyme.

Navigating the complex landscape of benzodiazepine- and Z-drug diversity: insights from comprehensive FDA adverse event reporting system analysis and beyond

Introduction

Medications which target benzodiazepine (BZD) binding sites of GABAA receptors (GABAARs) have been in widespread use since the nineteen-sixties. They carry labels as anxiolytics, hypnotics or antiepileptics. All benzodiazepines and several nonbenzodiazepine Z-drugs share high affinity binding sites on certain subtypes of GABAA receptors, from which they can be displaced by the clinically used antagonist flumazenil. Additional binding sites exist and overlap in part with sites used by some general anaesthetics and barbiturates. Despite substantial preclinical efforts, it remains unclear which receptor subtypes and ligand features mediate individual drug effects. There is a paucity of literature comparing clinically observed adverse effect liabilities across substances in methodologically coherent ways.

Methods

In order to examine heterogeneity in clinical outcome, we screened the publicly available U.S. FDA adverse event reporting system (FAERS) database for reports of individual compounds and analyzed them for each sex individually with the use of disproportionality analysis. The complementary use of physico-chemical descriptors provides a molecular basis for the analysis of clinical observations of wanted and unwanted drug effects.

Results and Discussion

We found a multifaceted FAERS picture, and suggest that more thorough clinical and pharmacoepidemiologic investigations of the heterogenous side effect profiles for benzodiazepines and Z-drugs are needed. This may lead to more differentiated safety profiles and prescription practice for particular compounds, which in turn could potentially ease side effect burden in everyday clinical practice considerably. From both preclinical literature and pharmacovigilance data, there is converging evidence that this very large class of psychoactive molecules displays a broad range of distinctive unwanted effect profiles - too broad to be explained by the four canonical, so-called "diazepam-sensitive high-affinity interaction sites". The substance-specific signatures of compound effects may partly be mediated by phenomena such as occupancy of additional binding sites, and/or synergistic interactions with endogenous substances like steroids and endocannabinoids. These in turn drive the wanted and unwanted effects and sex differences of individual compounds.

New aryl and acylsulfonamides as state-dependent inhibitors of Nav1.3 voltage-gated sodium channel

Voltage-gated sodium channels (Na_vs) play an essential role in neurotransmission, and their dysfunction is often a cause of various neurological disorders. The Na_v1.3 isoform is found in the CNS and upregulated after injury in the periphery, but its role in human physiology has not yet been fully elucidated. Reports suggest that selective Na_v1.3 inhibitors could be used as novel therapeutics to treat pain or neurodevelopmental disorders. Few selective inhibitors of this channel are known in the literature. In this work, we report the discovery of a new series of aryl and acylsulfonamides as state-dependent inhibitors of Na_v1.3 channels. Using a ligand-based 3D similarity search and subsequent hit optimization, we identified and prepared a series of 47 novel compounds and tested them on Na_v1.3, Na_v1.5, and a selected subset also on Na_v1.7 channels in a QPatch patch-clamp electrophysiology assay. Eight compounds had an IC₅₀ value of less than 1 μM against the Na_v1.3 channel inactivated state, with one compound displaying an IC₅₀ value of 20 nM, whereas activity against the inactivated state of the Na_v1.5 channel and Na_v1.7 channel was approximately 20-fold weaker. None of the compounds showed use-dependent inhibition of the cardiac isoform Na_v1.5 at a concentration of 30 μM. Further selectivity testing of the most promising hits was measured using the two-electrode voltage-clamp method against the closed state of the Na_v1.1-Na_v1.8 channels, and compound 15b displayed small, yet selective, effects against the Na_v1.3 channel, with no activity against the other isoforms. Additional selectivity testing of promising hits against the inactivated state of the Na_v1.3, Na_v1.7, and Na_v1.8 channels revealed several compounds with robust and selective activity against the inactivated state of the Na_v1.3 channel among the three isoforms tested. Moreover, the compounds were not cytotoxic at a concentration of 50 μM, as demonstrated by the assay in human HepG2 cells (hepatocellular carcinoma cells). The novel state-dependent inhibitors of Na_v1.3 discovered in this work provide a valuable tool to better evaluate this channel as a potential drug target.

Identification of novel antimicrobial compounds targeting Mycobacterium tuberculosis shikimate kinase using in silico hierarchical structure-based drug screening

The development of new anti-TB drugs to prevent the spread of multidrug-resistant Mycobacterium tuberculosis (Mtb) strains is imperative. Mtb shikimate kinase (MtSK) was selected as the target protein to screen for new anti-TB drugs. We performed hierarchical in silico screening using a library of 154,118 compounds to search for novel compounds that could bind to the active site of MtSK. The growth-inhibitory effects of the candidate compounds on Mycobacterium smegmatis were evaluated in vitro. Nine of the 11 candidate compounds exhibited inhibitory effects against mycobacteria in vitro. The inhibitory activity of Compound 2 (IC₅₀ = 1.39 μM) was higher than that of isoniazid, the first-line drug for TB treatment. Moreover, Compound 2 did not exhibit toxicity against mammalian cells and Escherichia coli. Molecular dynamics simulations using the MtSK-Compound 2 complex structure in a timeframe of 100 ns suggested that Compound 2 could stably bind to MtSK. The binding free energy of Compound 2 was estimated to be -37.96 kcal/mol using the MM/PBSA method, demonstrating that Compound 2 can stably bind to MtSK. These in silico and in vitro results indicated that Compound 2 is a promising hit compound for the development of novel anti-TB drugs.

Application of an Integrative Drug Safety Model for Detection of Adverse Drug Events Associated With Inhibition of Glutathione Peroxidase 1 in Chronic Obstructive Pulmonary Disease

BACKGROUND

Chronic Obstructive Pulmonary Disease is characterised by declining lung function and a greater oxidative stress burden due to reduced activity of antioxidant enzymes such as Glutathione Peroxidase 1.

OBJECTIVES

The extent to which drugs may contribute to this compromised activity is largely unknown. An integrative drug safety model explores inhibition of Glutathione Peroxidase 1 by drugs and their association with chronic obstructive pulmonary disease adverse drug events.

METHODS

In silico molecular modelling approaches were utilised to predict the interactions that drugs have within the active site of Glutathione Peroxidase 1 in both human and bovine models. Similarities of chemical features between approved drugs and the known inhibitor tiopronin were also investigated. Subsequently the Food and Drug Administration Adverse Event System was searched to uncover adverse drug event signals associated with chronic obstructive pulmonary disease.

RESULTS

Statistical and molecular modelling analyses confirmed that the use of several registered drugs, including acetylsalicylic acid and atenolol may be associated with inhibition of Glutathione Peroxidase 1 and chronic obstructive pulmonary disease.

CONCLUSION

The integration of molecular modelling and pharmacoepidemological data has the potential to advance drug safety science. Ongoing review of medication use and further pharmacoepidemiological and biological analyses are warranted to ensure appropriate use is recommended.

New Insights on the Activity and Selectivity of MAO-B Inhibitors through In Silico Methods

To facilitate the identification of novel MAO-B inhibitors, we elaborated a consolidated computational approach, including a pharmacophoric atom-based 3D quantitative structure-activity relationship (QSAR) model, activity cliffs, fingerprint, and molecular docking analysis on a dataset of 126 molecules. An AAHR.2 hypothesis with two hydrogen bond acceptors (A), one hydrophobic (H), and one aromatic ring (R) supplied a statistically significant 3D QSAR model reflected by the parameters: R² = 0.900 (training set); Q² = 0.774 and Pearson's R = 0.884 (test set), stability s = 0.736. Hydrophobic and electron-withdrawing fields portrayed the relationships between structural characteristics and inhibitory activity. The quinolin-2-one scaffold has a key role in selectivity towards MAO-B with an AUC of 0.962, as retrieved by ECFP4 analysis. Two activity cliffs showing meaningful potency variation in the MAO-B chemical space were observed. The docking study revealed interactions with crucial residues TYR:435, TYR:326, CYS:172, and GLN:206 responsible for MAO-B activity. Molecular docking is in consensus with and complementary to pharmacophoric 3D QSAR, ECFP4, and MM-GBSA analysis. The computational scenario provided here will assist chemists in quickly designing and predicting new potent and selective candidates as MAO-B inhibitors for MAO-B-driven diseases. This approach can also be used to identify MAO-B inhibitors from other libraries or screen top molecules for other targets involved in suitable diseases.

Hydrogen Bonding Shuts Down Tunneling in Hydroxycarbenes: A Gas-Phase Study by Tandem-Mass Spectrometry, Infrared Ion Spectroscopy, and Theory

Hydroxycarbenes can be generated and structurally characterized in the gas phase by collision-induced decarboxylation of α-keto carboxylic acids, followed by infrared ion spectroscopy. Using this approach, we have shown earlier that quantum-mechanical hydrogen tunneling (QMHT) accounts for the isomerization of a charge-tagged phenylhydroxycarbene to the corresponding aldehyde in the gas phase and above room temperature. Herein, we report the results of our current study on aliphatic trialkylammonio-tagged systems. Quite unexpectedly, the flexible 3-(trimethylammonio)propylhydroxycarbene turned out to be stable─no H-shift to either aldehyde or enol occurred. As supported by density functional theory calculations, this novel QMHT inhibition is due to intramolecular H-bonding of a mildly acidic α-ammonio C-H bonds to the hydroxyl carbene's C-atom (C:···H-C). To further support this hypothesis, (4-quinuclidinyl)hydroxycarbenes were synthesized, whose rigid structure prevents this intramolecular H-bonding. The latter hydroxycarbenes underwent "regular" QMHT to the aldehyde at rates comparable to, e.g., methylhydroxycarbene studied by Schreiner et al. While QMHT has been shown for a number of biological H-shift processes, its inhibition by H-bonding disclosed here may serve for the stabilization of highly reactive intermediates such as carbenes, even as a mechanism for biasing intrinsic selectivity patterns.