Approaching Pharmacological Space: Events and Components.

The pharmacological space comprises all the dynamic events that determine the bioactivity (and/or the metabolism and toxicity) of a given ligand. The pharmacological space accounts for the structural flexibility and property variability of the two interacting molecules as well as for the mutual adaptability characterizing their molecular recognition process. The dynamic behavior of all these events can be described by a set of possible states (e.g., conformations, binding modes, isomeric forms) that the simulated systems can assume. For each monitored state, a set of state-dependent ligand- and structure-based descriptors can be calculated. Instead of considering only the most probable state (as routinely done), the pharmacological space proposes to consider all the monitored states. For each state-dependent descriptor, the corresponding space can be evaluated by calculating various dynamic parameters such as mean and range values.The reviewed examples emphasize that the pharmacological space can find fruitful applications in structure-based virtual screening as well as in toxicity prediction. In detail, in all reported examples, the inclusion of the pharmacological space parameters enhances the resulting performances. Beneficial effects are obtained by combining both different binding modes to account for ligand mobility and different target structures to account for protein flexibility/adaptability.The proposed computational workflow that combines docking simulations and rescoring analyses to enrich the arsenal of docking-based descriptors revealed a general applicability regardless of the considered target and utilized docking engine. Finally, the EFO approach that generates consensus models by linearly combining various descriptors yielded highly performing models in all discussed virtual screening campaigns.

Computational Toxicology Methods in Chemical Library Design and High-Throughput Screening Hit Validation.

The discovery of molecular toxicity in a clinical drug candidate can have a significant impact on both the cost and timeline of the drug discovery process. Early identification of potentially toxic compounds during screening library preparation or, alternatively, during the hit validation process is critical to ensure that valuable time and resources are not spent pursuing compounds that may possess a high propensity for human toxicity. This report focuses on the application of computational molecular filters, applied either pre- or post-screening, to identify and remove known reactive and/or potentially toxic compounds from consideration in drug discovery campaigns.

Machine Learning in Early Prediction of Metabolism of Drugs.

Machine learning (ML) has increasingly been applied to predict properties of drugs. Particularly, metabolism can be predicted with ML methods, which can be exploited during drug discovery and development. The prediction of metabolism is a crucial bottleneck in the early identification of toxic metabolites or biotransformation pathways that can affect elimination of the drug and potentially hinder the development of future new drugs. Metabolism prediction can be addressed with the application of ML models trained on large and validated dataset, from early stages of lead optimization to latest stage of drug development. ML methods rely on molecular descriptors that allow to identify and learn chemical and molecular features to predict sites of metabolism (SoMs) or activity associated with mechanism of inhibition (e.g., CYP inhibition). The application of ML methods in the prediction of drug metabolism represents a powerful resource to be exploited during drug discovery and development. ML allows to improve in silico screening and safety assessments of drugs in advance, steering their path to marketing authorization. Prediction of biotransformation reactions and metabolites allows to shorten the time, save the cost, and reduce animal testing. In this context, ML methods represent a technique to fill data gaps and an opportunity to reduce animal testing, calling for the 3R principles within the Big Data era.

PFAS-Biomolecule Interactions: Case Study Using Asclepios Nodes and Automated Workflows in KNIME for Drug Discovery and Toxicology.

The Asclepios suite of KNIME nodes represents an innovative solution for conducting cheminformatics and computational chemistry tasks, specifically tailored for applications in drug discovery and computational toxicology. This suite has been developed using open-source and publicly accessible software. In this chapter, we introduce and explore the Asclepios suite through the lens of a case study. This case study revolves around investigating the interactions between per- and polyfluorinated alkyl substances (PFAS) and biomolecules, such as nuclear receptors. The objective is to characterize the potential toxicity of PFAS and gain insights into their chemical mode of action at the molecular level. The Asclepios KNIME nodes have been designed as versatile tools capable of addressing a wide range of computational toxicology challenges. Furthermore, they can be adapted and customized to accomodate the specific needs of individual users, spanning various domains such as nanoinformatics, biomedical research, and other related applications. This chapter provides an in-depth examination of the technical underpinnings and foundations of these tools. It is accompanied by a practical case study that demonstrates the utilization of Asclepios nodes in a computational toxicology investigation. This showcases the extendable functionalities that can be applied in diverse computational chemistry contexts. By the end of this chapter, we aim for readers to have a comprehensive understanding of the effectiveness of the Asclepios node functions. These functions hold significant potential for enhancing a wide spectrum of cheminformatics applications.

CHARMM-GUI PDB Reader and Manipulator: Covalent Ligand Modeling and Simulation.

Molecular modeling and simulation serve an important role in exploring biological functions of proteins at the molecular level, which is complementary to experiments. CHARMM-GUI (https://www.charmm-gui.org) is a web-based graphical user interface that generates complex molecular simulation systems and input files, and we have been continuously developing and expanding its functionalities to facilitate various complex molecular modeling and make molecular dynamics simulations more accessible to the scientific community. Currently, covalent drug discovery emerges as a popular and important field. Covalent drug forms a chemical bond with specific residues on the target protein, and it has advantages in potency for its prolonged inhibition effects. Even though there are higher demands in modeling PDB protein structures with various covalent ligand types, proper modeling of covalent ligands remains challenging. This work presents a new functionality in CHARMM-GUI PDB Reader & Manipulator that can handle a diversity of ligand-amino acid linkage types, which is validated by a careful benchmark study using over 1,000 covalent ligand structures in RCSB PDB. We hope that this new functionality can boost the modeling and simulation study of covalent ligands.

PD-1 Targeted Antibody Discovery Using AI Protein Diffusion.

The programmed cell death protein 1 (PD-1, CD279) is an important therapeutic target in many oncological diseases. This checkpoint protein inhibits T lymphocytes from attacking other cells in the body and thus blocking it improves the clearance of tumor cells by the immune system. While there are already multiple FDA-approved anti-PD-1 antibodies, including nivolumab (Opdivo® from Bristol-Myers Squibb) and pembrolizumab (Keytruda® from Merck), there are ongoing efforts to discover new and improved checkpoint inhibitor therapeutics. In this study, we present multiple anti-PD-1 antibody fragments that were derived computationally using protein diffusion and evaluated through our scalable, in silico pipeline. Here we present nine synthetic Fv structures that are suitable for further empirical testing of their anti-PD-1 activity due to desirable predicted binding performance.

Open-Source Approach to GPU-Accelerated Substructure Search.

Chemical substructure search is a critical task in medicinal chemistry and small-molecule drug discovery, enabling the retrieval of molecules from databases based on specific chemical features. While systems exist for this purpose, the challenge of efficient and swift searching persists, particularly as data storage migrates to the cloud, introducing new complexities. This study provides a comprehensive analysis of chemical substructure searches, showcasing the benefits of graphics processing unit-accelerated fingerprint screening. The research highlights strategies for optimizing performance, making significant advancements in substructure searching, a pivotal aspect of drug discovery and molecular research. The accessible and scalable nature of the proposed approach makes it a valuable resource for scientists aiming to enhance their substructure search capabilities.

Improvements in Precision of Relative Binding Free Energy Calculations Afforded by the Alchemical Enhanced Sampling (ACES) Approach.

Accurate in silico predictions of how strongly small molecules bind to proteins, such as those afforded by relative binding free energy (RBFE) calculations, can greatly increase the efficiency of the hit-to-lead and lead optimization stages of the drug discovery process. The success of such calculations, however, relies heavily on their precision. Here, we show that a recently developed alchemical enhanced sampling (ACES) approach can consistently improve the precision of RBFE calculations on a large and diverse set of proteins and small molecule ligands. The addition of ACES to conventional RBFE calculations lowered the average hysteresis by over 35% (0.3-0.4 kcal/mol) and the average replicate spread by over 25% (0.2-0.3 kcal/mol) across a set of 10 protein targets and 213 small molecules while maintaining similar or improved accuracy. We show in atomic detail how ACES improved convergence of several representative RBFE calculations through enhancing the sampling of important slowly transitioning ligand degrees of freedom.

The discovery and development of GLP-1 based drugs that have revolutionized the treatment of obesity.

The 2024 Lasker~DeBakey Clinical Medical Research Award has been given to Joel Habener and Svetlana Mojsov for their discovery of a new hormone GLP-1(7-37) and to Lotte Knudsen for her role in developing sustained acting versions of this hormone as a treatment for obesity. Each of the three had a distinct set of skills that made this advance possible; Habener is an endocrinologist and molecular biologist, Mojsov is a peptide chemist, and Knudsen is a pharmaceutical scientist. Their collective efforts have done what few thought possible-the development of highly effective medicines for reducing weight. Their research has also solved a mystery that began more than a century ago.

Discovery of a urea-based hit compound as a novel inhibitor of transforming growth factor-ß type 1 receptor: in silico and in vitro studies.

Transforming growth factor ß type 1 receptor (TGFßR1), a crucial serine-threonine kinase, is central to the TGFß/Smad signaling pathway, governing cellular processes like growth, differentiation, apoptosis, and immune response. This pathway is closely linked to the epithelial-mesenchymal transition (EMT) process, which plays an important role in the metastasis of hepatocellular carcinoma (HCC). To date, only limited inhibitors targeting TGFßR1 have entered clinical trials, yet they encounter challenges, notably high toxicity, in clinical applications. Herein, an efficient virtual screening pipeline was developed. Eighty compounds were screened from a pool of over 17 million molecules based on docking scores and binding free energy. Four compounds were manually selected with the assistance of enhanced sampling method BPMD (binding pose metadynamics). The binding stability of these four compounds complexed with TGFßR1 was subsequently studied through long-timescale conventional molecular dynamics simulations. The three most promising compounds were subjected to in vitro bioactivity assays. Cpd272 demonstrated moderate inhibitory activity against TGFßR1, with an IC50 value of 1.57 ± 0.33 µM. Moreover, it exhibited cytotoxic effects on human hepatocellular carcinoma cell line Bel-7402. By shedding light on the binding mode of the receptor-ligand complexes, Cpd272 was identified as a hit compound featuring a novel urea-based scaffold capable of effectively inhibiting TGFßR1.

PGBind: pocket-guided explicit attention learning for protein-ligand docking.

As more and more protein structures are discovered, blind protein-ligand docking will play an important role in drug discovery because it can predict protein-ligand complex conformation without pocket information on the target proteins. Recently, deep learning-based methods have made significant advancements in blind protein-ligand docking, but their protein features are suboptimal because they do not fully consider the difference between potential pocket regions and non-pocket regions in protein feature extraction. In this work, we propose a pocket-guided strategy for guiding the ligand to dock to potential docking regions on a protein. To this end, we design a plug-and-play module to enhance the protein features, which can be directly incorporated into existing deep learning-based blind docking methods. The proposed module first estimates potential pocket regions on the target protein and then leverages a pocket-guided attention mechanism to enhance the protein features. Experiments are conducted on integrating our method with EquiBind and FABind, and the results show that their blind-docking performances are both significantly improved and new start-of-the-art performance is achieved by integration with FABind.

Discovery of the potent covalent inhibitor with an acrylate warhead for SARS-CoV-2 3CL protease.

COVID-19 has caused severe consequences in terms of public health and economy worldwide since its outbreak in December 2019. SARS-CoV-2 3C-like protease (3CLpro), crucial for the viral replications, is an attractive target for the development of antiviral drugs. In this study, several kinds of Michael acceptor warheads were utilized to hunt for potent covalent inhibitors against 3CLpro. Meanwhile, novel 3CLpro inhibitors with the P3-3,5-dichloro-4-(2-(dimethylamino)ethoxy)phenyl moiety were designed and synthesized which may form salt bridge with residue Glu166. Among them, two compounds 12b and 12c exhibited high inhibitory activities against SARS-CoV-2 3CLpro. Further investigations suggested that 12b with an acrylate warhead displayed potent activity against HCoV-OC43 (EC50 = 97 nM) and SARS-CoV-2 replicon (EC50 = 45 nM) and low cytotoxicity (CC50 > 10 µM) in Huh7 cells. Taken together, this study devised two series of 3CLpro inhibitors and provided the potent SARS-CoV-2 3CLpro inhibitor (12b) which may be used for treating coronavirus infections.

Discovery and Preclinical Profile of ALG-055009, a Potent and Selective Thyroid Hormone Receptor Beta (THR-ß) Agonist for the Treatment of MASH.

Agonists of thyroid hormone receptor ß (THR-ß) decreased LDL cholesterol (LDL-C) and triglyceride (TG) levels in human clinical trials for patients with dyslipidemia. The authors present the highly potent and selective compound ALG-055009 (14) as a potential best in class THR-ß agonist. The high metabolic stability and good permeability translated well in vivo to afford a long in vivo half-life pharmacokinetic profile with limited liability for DDI, and it overcomes certain drawbacks seen in recent clinical candidates.

Enhancing Drug Discovery and Development through the Integration of Medicinal Chemistry, Chemical Biology, and Academia-Industry Partnerships: Insights from Roche's Endocannabinoid System Projects.

The endocannabinoid system (ECS) is a critical regulatory network composed of endogenous cannabinoids (eCBs), their synthesizing and degrading enzymes, and associated receptors. It is integral to maintaining homeostasis and orchestrating key functions within the central nervous and immune systems. Given its therapeutic significance, we have launched a series of drug discovery endeavors aimed at ECS targets, including peroxisome proliferator-activated receptors (PPARs), cannabinoid receptors types 1 (CB1R) and 2 (CB2R), and monoacylglycerol lipase (MAGL), addressing a wide array of medical needs. The pursuit of new therapeutic agents has been enhanced by the creation of specialized labeled chemical probes, which aid in target localization, mechanistic studies, assay development, and the establishment of biomarkers for target engagement. By fusing medicinal chemistry with chemical biology in a comprehensive, translational end-to-end drug discovery strategy, we have expedited the development of novel therapeutics. Additionally, this strategy promises to foster highly productive partnerships between industry and academia, as will be illustrated through various examples.

Discovery of a proteolysis targeting chimera (PROTAC) as a potent regulator of FOXP3.

Regulatory T (Treg) cells play a central role in immune homeostasis. Forkhead box P3 (Foxp3), a hallmark molecule in Treg cells, is a vital transcription factor for their development and function. Studies have shown that degradation of the Foxp3 could provide therapeutic benefits in achieving effective anti-tumor immunity. In this study, we designed three PROTAC molecules, P60-L1-VHL, P60-L2-VHL, and P60-L3-VHL, based on a 15-mer peptide inhibitor of Foxp3 (P60), and explored their potential in regulating Foxp3 expression and function. Our data show that, among these molecules, P60-L3-VHL can inhibit the expression and nuclear localization of Foxp3 in HEK 293 T and HeLa cells, respectively. Meanwhile, use of proteasome inhibitor in P60-L3-VHL treated cells revealed an increased Foxp3 expression, indicating that P60-L3-VHL mediates the inhibition of Foxp3 through its degradation in the proteasome pathway. We further substantiate that P60-L3-VHL reduces the differentiation and Foxp3 expression in the in-vitro activated Treg cells. Overall, our findings suggest that P60-L3-VHL inhibits the differentiation of Treg cells by degrading the Foxp3, which may have potential implications in cancer immunotherapy.

Discovery and preclinical profile of YK-2168, a differentiated selective CDK9 inhibitor in clinical development.

Emerging clinical evidence indicates that selective CDK9 inhibition may provide clinical benefits in the management of certain cancers. Many CDK9 selective inhibitors have entered clinical developments, and are being investigated. No clear winner has emerged because of unforeseen toxicity often observed in clinic with these agents. Therefore, a novel agent with differentiated profiles is still desirable. Herein, we report our design, syntheses of a novel azaindole series of selective CDK9 inhibitors. SAR studies led to a preclinical candidate YK-2168. YK2168 exhibited improved CDK9 selectivity over AZD4573 and BAY1251152; also showed differentiated intravenous PK profile and remarkable solid tumor efficacy in a mouse gastric cancer SNU16 CDX model in preclinical studies. YK-2168 is currently in clinical development in China (CTR20212900).

Recent Advances: Heterocycles in Drugs and Drug Discovery.

Interest and research focusing on the design of novel pharmaceutical agents is always growing [...].

Non-Negative Matrix Tri-Factorization for Representation Learning in Multi-Omics Datasets with Applications to Drug Repurposing and Selection.

The vast corpus of heterogeneous biomedical data stored in databases, ontologies, and terminologies presents a unique opportunity for drug design. Integrating and fusing these sources is essential to develop data representations that can be analyzed using artificial intelligence methods to generate novel drug candidates or hypotheses. Here, we propose Non-Negative Matrix Tri-Factorization as an invaluable tool for integrating and fusing data, as well as for representation learning. Additionally, we demonstrate how representations learned by Non-Negative Matrix Tri-Factorization can effectively be utilized by traditional artificial intelligence methods. While this approach is domain-agnostic and applicable to any field with vast amounts of structured and semi-structured data, we apply it specifically to computational pharmacology and drug repurposing. This field is poised to benefit significantly from artificial intelligence, particularly in personalized medicine. We conducted extensive experiments to evaluate the performance of the proposed method, yielding exciting results, particularly compared to traditional methods. Novel drug-target predictions have also been validated in the literature, further confirming their validity. Additionally, we tested our method to predict drug synergism, where constructing a classical matrix dataset is challenging. The method demonstrated great flexibility, suggesting its applicability to a wide range of tasks in drug design and discovery.

Computational Screening of T-Muurolol for an Alternative Antibacterial Solution against Staphylococcus aureus Infections: An In Silico Approach for Phytochemical-Based Drug Discovery.

Staphylococcus aureus infections present a significant threat to the global healthcare system. The increasing resistance to existing antibiotics and their limited efficacy underscores the urgent need to identify new antibacterial agents with low toxicity to effectively combat various S. aureus infections. Hence, in this study, we have screened T-muurolol for possible interactions with several S. aureus-specific bacterial proteins to establish its potential as an alternative antibacterial agent. Based on its binding affinity and interactions with amino acids, T-muurolol was identified as a potential inhibitor of S. aureus lipase, dihydrofolate reductase, penicillin-binding protein 2a, D-Ala:D-Ala ligase, and ribosome protection proteins tetracycline resistance determinant (RPP TetM), which indicates its potentiality against S. aureus and its multi-drug-resistant strains. Also, T-muurolol exhibited good antioxidant and anti-inflammatory activity by showing strong binding interactions with flavin adenine dinucleotide (FAD)-dependent nicotinamide adenine dinucleotide phosphate (NAD(P)H) oxidase, and cyclooxygenase-2. Consequently, molecular dynamics (MD) simulation and recalculating binding free energies elucidated its binding interaction stability with targeted proteins. Furthermore, quantum chemical structure analysis based on density functional theory (DFT) depicted a higher energy gap between the highest occupied molecular orbital and lowest unoccupied molecular orbital (EHOMO-LUMO) with a lower chemical potential index, and moderate electrophilicity suggests its chemical hardness and stability and less polarizability and reactivity. Additionally, pharmacological parameters based on ADMET, Lipinski's rules, and bioactivity score validated it as a promising drug candidate with high activity toward ion channel modulators, nuclear receptor ligands, and enzyme inhibitors. In conclusion, the current findings suggest T-muurolol as a promising alternative antibacterial agent that might be a potential phytochemical-based drug against S. aureus. This study also suggests further clinical research before human application.

Adapting differential molecular representation with hierarchical prompts for multi-label property prediction.

Accurate prediction of molecular properties is crucial in drug discovery. Traditional methods often overlook that real-world molecules typically exhibit multiple property labels with complex correlations. To this end, we propose a novel framework, HiPM, which stands for Hierarchical Prompted Molecular representation learning framework. HiPM leverages task-aware prompts to enhance the differential expression of tasks in molecular representations and mitigate negative transfer caused by conflicts in individual task information. Our framework comprises two core components: the Molecular Representation Encoder (MRE) and the Task-Aware Prompter (TAP). MRE employs a hierarchical message-passing network architecture to capture molecular features at both the atom and motif levels. Meanwhile, TAP utilizes agglomerative hierarchical clustering algorithm to construct a prompt tree that reflects task affinity and distinctiveness, enabling the model to consider multi-granular correlation information among tasks, thereby effectively handling the complexity of multi-label property prediction. Extensive experiments demonstrate that HiPM achieves state-of-the-art performance across various multi-label datasets, offering a novel perspective on multi-label molecular representation learning.