Using ChEMBL to Complement Schistosome Drug Discovery

Discovery and characterization of novel FAK inhibitors for breast cancer therapy via hybrid virtual screening, biological evaluation and molecular dynamics simulations

Focal adhesion kinase (FAK) is a critical drug target implicated in various disease pathways, including hematological malignancies and breast cancer. Therefore, identifying FAK inhibitors with novel scaffolds could offer new opportunities for developing effective therapeutic compounds. Herein, we disclosed the discovery of a new backbone inhibitor of FAK using an "internal" database, employing a structure-based high-transparency permeability virtual screening (HTVS) and a DeepDock algorithm based on geometric deep learning. Subsequently, molecular docking was conducted at different precisions to identify 10 compounds for further evaluation of biological activity. Ultimately, compound 4, a pyrimidin-4-amine derivative, demonstrated inhibitory activity against FAK and breast cancer cells, further supporting its potential as a FAK inhibitor. Moreover, molecular dynamics simulations were carried out to gain more detailed insights into the binding mechanism between compound 4 and FAK to guide subsequent structural optimization.

Discovery of Novel DDR1 Inhibitors through a Hybrid Virtual Screening Pipeline, Biological Evaluation and Molecular Dynamics Simulations

Acute myeloid leukemia (AML) is a heterogeneous hematopoietic malignancy with limited therapeutic options for many patients. Discoidin domain receptor 1 (DDR1), a transmembrane tyrosine kinase receptor, has been implicated in AML progression and represents a promising therapeutic target. In this study, we employed a hybrid virtual screening workflow that integrates deep learning-based binding affinity predictions with molecular docking techniques to identify potential DDR1 inhibitors. A multistage screening process involving PSICHIC, KarmaDock, Vina-GPU, and similarity-based scoring was conducted, leading to the selection of seven candidate compounds. The biological evaluation identified Compound 4 as a novel DDR1 inhibitor, demonstrating significant DDR1 inhibitory activity with an IC50 of 46.16 nM and a 99.86% inhibition rate against Z-138 cells at 10 μM. Molecular dynamics simulations and binding free energy calculations further validated the stability and strong binding interactions of Compound 4 with DDR1. This study highlights the utility of combining deep learning models with traditional molecular docking techniques to accelerate the discovery of potent and selective DDR1 inhibitors. The identified compounds hold promise for further development as targeted therapies for AML.

Computational drug repurposing: approaches, evaluation of in silico resources and case studies

Repurposing of existing drugs for new indications has attracted substantial attention owing to its potential to accelerate drug development and reduce costs. Hundreds of computational resources such as databases and predictive platforms have been developed that can be applied for drug repurposing, making it challenging to select the right resource for a specific drug repurposing project. With the aim of helping to address this challenge, here we overview computational approaches to drug repurposing based on a comprehensive survey of available in silico resources using a purpose-built drug repurposing ontology that classifies the resources into hierarchical categories and provides application-specific information. We also present an expert evaluation of selected resources and three drug repurposing case studies implemented within the Horizon Europe REMEDi4ALL project to demonstrate the practical use of the resources. This comprehensive Review with expert evaluations and case studies provides guidelines and recommendations on the best use of various in silico resources for drug repurposing and establishes a basis for a sustainable and extendable drug repurposing web catalogue.

Deep learning based predictive modeling to screen natural compounds against TNF-alpha for the potential management of rheumatoid arthritis: Virtual screening to comprehensive in silico investigation

Rheumatoid arthritis (RA) affects an estimated 0.1% to 2.0% of the world's population, leading to a substantial impact on global health. The adverse effects and toxicity associated with conventional RA treatment pathways underscore the critical need to seek potential new therapeutic candidates, particularly those of natural sources that can treat the condition with minimal side effects. To address this challenge, this study employed a deep-learning (DL) based approach to conduct a virtual assessment of natural compounds against the Tumor Necrosis Factor-alpha (TNF-α) protein. TNF-α stands out as the primary pro-inflammatory cytokine, crucial in the development of RA. Our predictive model demonstrated appreciable performance, achieving MSE of 0.6, MAPE of 10%, and MAE of 0.5. The model was then deployed to screen a comprehensive set of 2563 natural compounds obtained from the Selleckchem database. Utilizing their predicted bioactivity (pIC50), the top 128 compounds were identified. Among them, 68 compounds were taken for further analysis based on drug-likeness analysis. Subsequently, selected compounds underwent additional evaluation using molecular docking (< - 8.7 kcal/mol) and ADMET resulting in four compounds posing nominal toxicity, which were finally subjected to MD simulation for 200 ns. Later on, the stability of complexes was assessed via analysis encompassing RMSD, RMSF, Rg, H-Bonds, SASA, and Essential Dynamics. Ultimately, based on the total binding free energy estimated using the MM/GBSA method, Imperialine, Veratramine, and Gelsemine are proven to be potential natural inhibitors of TNF-α.

The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges

Due to the transformation of artificial intelligence (AI) tools and technologies, AI-driven drug discovery has come to the forefront. It reduces the time and expenditure. Due to these advantages, pharmaceutical industries are concentrating on AI-driven drug discovery. Several drug molecules have been discovered using AI-based techniques and tools, and several newly AI-discovered drug molecules have already entered clinical trials. In this review, we first present the data and their resources in the pharmaceutical sector for AI-driven drug discovery and illustrated some significant algorithms or techniques used for AI and ML which are used in this field. We gave an overview of the deep neural network (NN) models and compared them with artificial NNs. Then, we illustrate the recent advancement of the landscape of drug discovery using AI to deep learning, such as the identification of drug targets, prediction of their structure, estimation of drug-target interaction, estimation of drug-target binding affinity, design of de novo drug, prediction of drug toxicity, estimation of absorption, distribution, metabolism, excretion, toxicity; and estimation of drug-drug interaction. Moreover, we highlighted the success stories of AI-driven drug discovery and discussed several collaboration and the challenges in this area. The discussions in the article will enrich the pharmaceutical industry.

Small Molecule Ligands of the BET-like Bromodomain, SmBRD3, Affect Schistosoma mansoni Survival, Oviposition, and Development

Schistosomiasis is a disease affecting >200 million people worldwide, but its treatment relies on a single agent, praziquantel. To investigate new avenues for schistosomiasis control, we have conducted the first systematic analysis of bromodomain-containing proteins (BCPs) in a causative species, Schistosoma mansoni. Having identified 29 putative bromodomains (BRDs) in 22 S. mansoni proteins, we selected SmBRD3, a tandem BRD-containing BCP that shows high similarity to the human bromodomain and extra terminal domain (BET) family, for further studies. Screening 697 small molecules identified the human BET BRD inhibitor I-BET726 as a ligand for SmBRD3. An X-ray crystal structure of I-BET726 bound to the second BRD of SmBRD3 [SmBRD3(2)] enabled rational design of a quinoline-based ligand (15) with an ITC Kd = 364 ± 26.3 nM for SmBRD3(2). The ethyl ester pro-drug of compound 15 (compound 22) shows substantial effects on sexually immature larval schistosomula, sexually mature adult worms, and snail-infective miracidia in ex vivo assays.

Understanding anthelmintic resistance in livestock using "omics" approaches

Widespread and improper use of various anthelmintics, genetic, and epidemiological factors has resulted in anthelmintic-resistant (AR) helminth populations in livestock. This is currently quite common globally in different livestock animals including sheep, goats, and cattle to gastrointestinal nematode (GIN) infections. Therefore, the mechanisms underlying AR in parasitic worm species have been the subject of ample research to tackle this challenge. Current and emerging technologies in the disciplines of genomics, transcriptomics, metabolomics, and proteomics in livestock species have advanced the understanding of the intricate molecular AR mechanisms in many major parasites. The technologies have improved the identification of possible biomarkers of resistant parasites, the ability to find actual causative genes, regulatory networks, and pathways of parasites governing the AR development including the dynamics of helminth infection and host-parasite infections. In this review, various "omics"-driven technologies including genome scan, candidate gene, quantitative trait loci, transcriptomic, proteomic, and metabolomic approaches have been described to understand AR of parasites of veterinary importance. Also, challenges and future prospects of these "omics" approaches are also discussed.

Artificial intelligence-guided approach for efficient virtual screening of hits against Schistosoma mansoni

Background: The impact of schistosomiasis, which affects over 230 million people, emphasizes the urgency of developing new antischistosomal drugs. Artificial intelligence is vital in accelerating the drug discovery process. Methodology & results: We developed classification and regression machine learning models to predict the schistosomicidal activity of compounds not experimentally tested. The prioritized compounds were tested on schistosomula and adult stages of Schistosoma mansoni. Four compounds demonstrated significant activity against schistosomula, with 50% effective concentration values ranging from 9.8 to 32.5 μM, while exhibiting no toxicity in animal and human cell lines. Conclusion: These findings represent a significant step forward in the discovery of antischistosomal drugs. Further optimization of these active compounds can pave the way for their progression into preclinical studies.