AI-ASSISTED DRUG DISCOVERY TARGETING GENETIC PATHWAYS: INTEGRATING MOLECULAR BIOLOGY AND COMPUTATIONAL CHEMISTRY

Network analysis and drug repurposing reveal targets for combination therapy in drug resistant breast cancer

Multidrug resistance remains a major obstacle in cancer therapy and is driven by hyperactive efflux transporters and dysregulated signaling pathways. In this study, text mining and network analyses were performed to identify key genes associated with breast cancer drug resistance. Critical regulatory nodes were identified using the Boykov-Kolmogorov algorithm applied to a directed protein–protein interaction network. Molecular docking and molecular dynamics simulations were subsequently conducted to screen FDA-approved drugs for potential interactions with these targets. Cytotoxicity, migration, apoptosis, efflux activity, and relative gene expression assays were performed in drug-resistant and parental breast and gastric cancer cell lines to experimentally evaluate drug effects. ESR1, PPARD, and NFKB1 were identified as essential cut nodes sustaining MDR network connectivity. Drug repurposing analyses predicted celecoxib, desloratadine, and dutasteride as ligands targeting these proteins. Experimental validation demonstrated that the triple-drug combination significantly increased mitoxantrone sensitivity ( P  < 0.001) in multidrug-resistant breast cancer cells. This effect was accompanied by marked inhibition of drug efflux, including significant suppression of BCRP activity ( P  < 0.05) and up to a 20-fold reduction in BCRP gene expression ( P  < 0.001), while more limited effects were observed on MDR1 expression in gastric cells. Collectively, the combination treatment restored chemotherapy responsiveness, reduced cell migration, and promoted apoptosis. Overall, this study suggests that integrating network-based analysis with drug repurposing may provide a useful framework for identifying potential multidrug strategies against drug resistance. These findings offer a computational basis for further experimental validation and potential development of anti-resistance therapeutic approaches.

An AI platform to accelerate drug discovery: integrating target discovery, molecular generation, and simulation

Declining research and development productivity is a structural challenge in the pharmaceutical industry, where the discovery, optimization, and clinical evaluation of a single medicine require long timelines, high cost, and repeated decision-making under uncertainty. Artificial intelligence (AI), big data analysis, and computational simulation are expected to accelerate drug discovery, but isolated applications cannot by themselves transform the whole process. This invited narrative review and platform perspective summarizes the concept and current implementation of an integrated Drug Discovery DX Platform (DXPF) that connects disease information and patient omics data to target gene/protein discovery, lead compound generation, and simulation-based evaluation. In the target-discovery component, Bayesian network and graph-based methods infer diseasespecific regulatory networks from omics data and support identification of mechanisms and candidate targets. In the drug-design component, graph convolutional neural network-based compound profile prediction is coupled with ChemTS molecular generation, docking, and molecular dynamics simulation to address multi-objective optimization of potency, absorption, distribution, metabolism, excretion (ADME), toxicity, and structural binding behavior. The platform also incorporates integrated databases and federated learning developed through academia-industry collaboration, enabling model improvement while preserving confidential company data. By connecting these modules through a browserbased interface, DXPF aims to shorten an end-to-end computational cycle from analysisready patient data to prioritized candidate structures; the approximately one-week objective is an engineering target under favorable input and computing conditions and excludes compound synthesis and biological, pharmacokinetic, toxicological, and clinical validation. Current evidence is mainly module-level, retrospective, or computational, and prospective benchmarking, uncertainty assessment, and experimental validation are required before effects on attrition or regulatory decision-making can be established. Future expansion to biologics, disease-prevention AI, and preclinical and clinical data could broaden the platform's scope, but these extensions remain development goals.

Artificial Intelligence in Druggable Protein Identification: Data-driven Multidimensional Representations and the Evolution of Predictive Models.

Druggable proteins are proteins that can be specifically bound and modulated by drug molecules, with such modulation expected to produce therapeutic effects. The identification and validation of druggable proteins are central steps in modern drug discovery. With the rapid advancements in biological big data and artificial intelligence, computational methods based on bioinformatics have become essential for large-scale screening of druggable proteins. This review systematically summarizes the latest research progress in this field, providing a comprehensive analysis across three dimensions: data resources, feature engineering, and predictive models. The main contributions are 4-fold: First, we systematically describe three major types of databases: pharmacological annotation databases, quantitative affinity databases, and multi-source integrated databases, and dataset construction strategies as well as strategies for handling imbalanced samples are explored. Second, multi-level feature representations across four dimensions are categorized into sequencederived features, physicochemical properties, evolutionary information, and structural information, and the corresponding methods and tools are summarized. Third, we provide a detailed review of the evolution and application paradigms of predictive models, from classical machine learning and ensemble learning to deep learning architectures and protein large language models. Fourth, based on a systematic evaluation of representative studies, current limitations in areas such as few-shot learning and model interpretability are highlighted, and offer a forward-looking perspective on future research directions. This review aims to serve as a comprehensive reference for druggable protein prediction algorithms and to provide a solid theoretical and technical roadmap for computationally driven discovery of novel drug targets.

Drug Discovery from Gene Expression/Gene Regulation.

Precision Drug Discovery in the Era of Artificial Intelligence: A Critical Review.

Traditional drug development suffers from high costs, low success rates, and patient response variability. Precision drug discovery seeks to overcome these limitations by targeting specific genetic and molecular mechanisms but faces challenges in integrating cross-scale, multimodal biomedical data. Recent advances in artificial intelligence (AI), especially deep learning, provide powerful tools to navigate these complexities. This review surveys representative AI methods across four core stages of precision drug discovery: (a) target identification and validation using omics-, drug-, and structure-based approaches; (b) structure- and sequence-guided virtual screening of active compounds; (c) individualized drug response prediction integrating cell-line, single-cell, and multimodal data; and (d) AI-enabled toxicology and safety modeling to anticipate adverse liabilities and improve translational success. We further highlight a paradigm shift toward autonomous scientific agents capable of causal reasoning and end-to-end experimental guidance. Finally, we discuss persistent challenges, including data bias, limited interpretability, and in silico-to-wet lab translation.

Identification of Potential Therapeutic Targets and Drugs for Depression: A Genome-Wide Druggable Mendelian Randomization and Molecular Docking Study.

INTRODUCTION Depression is a common and complex mental health condition. It is shaped by many genetic, environmental, and social factors that influence when it starts and how it progresses. The main biological changes behind depression are still not well understood. Current treatments help some patients, but many only experience partial or unstable benefits. There is a clear need for treatments that are both more effective and more precise for depression. In this study, up-to-date genetic and pharmacogenomic data were used to identify genes targeted by drugs and potential therapy targets for depression. Information from several independent resources was brought together, and these datasets were combined in a stepwise way. With this strategy, the aim was to provide a practical starting point for the development of more targeted antidepressant therapies. METHODS Cross-tissue expression quantitative trait loci (eQTL) were first used to analyze data, and these data were combined with Genome-Wide Association Study (GWAS) summary statistics for depression. Then Mendelian Randomization (MR) was applied to identify candidate druggable genes. Colocalization analyses were also performed to check whether the eQTL and depression signals were likely to share the same causal variants. To further assess biological and therapeutic relevance, Phenome-Wide Association Studies (PheWAS), functional enrichment analyses, computational drug prediction, molecular docking, and molecular dynamics simulations were carried out. RESULTS Using Mendelian randomization, 34 blood-derived genes and 25 brain-derived genes were identified that showed evidence of association with depression. Colocalization further supported causal roles for 23 blood-and 14 brain-derived genes, with SNRK, MANF, and P4HTM showing significant signals in both tissues. Enrichment analyses implicated immune-related pathways. Docking and dynamics confirmed stable binding between key targets and compounds such as quercetagetin and myricetin. DISCUSSION In this study, 34 potential druggable targets for depression were found, with SNRK, MANF, and P4HTM showing consistent importance across tissues. In addition, quercetagetin and myricetin emerged as promising candidates for depression therapy. CONCLUSION These findings provide new therapeutic candidates and help establish a mechanistic framework for the development of targeted antidepressants.