Toward Efficient Drug Target Identification via Quantum-Enhanced Evolutionary Latent Modelling

Perspectives on Hybridizing Quantum Mechanics and Artificial Intelligence for Drug Design.

Systematic Benchmarking of AI-Based Molecular Generation Models for Structure-Based Drug Design

A state-aware functional classifier (SAFC) is developed that integrates molecular dynamics derived receptor ensembles, ensemble docking and protein ligand interaction graphs that provides dynamics-aware functional activity rankings for generated molecules that were partly complementary to docking, drug-likeness and synthetic accessibility scores.

Computational Strategies for Discovering Inhibitors Targeting Therapeutic Enzymes.

How Can Structure-Based Computational Methods Support Antibiotic Drug Discovery in Addressing Antimicrobial Resistance?

Antimicrobial resistance (AMR) has emerged as a major global health challenge, necessitating the development of innovative strategies to accelerate antibiotic drug discovery. Traditional drug development approaches are often time-consuming, costly, and resource-intensive, creating a growing need for computational methods that can improve research efficiency and therapeutic candidate identification. This study investigates the role of computational protein structure prediction in advancing antibiotic drug discovery through a conceptual literature review of recent research published between 2022 and 2026. The review examines the application of protein structure prediction techniques, including deep learning, machine learning, molecular simulations, and AlphaFold-based approaches, in supporting structure-based drug design. Particular attention is given to their contributions in drug target identification, binding site prediction, protein–ligand interaction analysis, virtual screening, and candidate prioritization. The findings indicate that computational approaches significantly enhance the efficiency of early-stage drug discovery by enabling accurate structural modeling, rapid screening of large compound libraries, and improved identification of promising antibacterial targets. Furthermore, the integration of artificial intelligence with molecular modeling techniques has strengthened prediction accuracy and facilitated the discovery of novel therapeutic candidates against drug-resistant pathogens. Despite these advancements, challenges related to prediction reliability, biological complexity, computational resource requirements, and dependence on high-quality datasets continue to affect the robustness of current approaches. Additionally, the reviewed studies emphasize the necessity of experimental validation to confirm computational findings and ensure clinical applicability. Overall, the study concludes that computational protein structure prediction plays a critical role in accelerating antibiotic drug discovery and offers substantial potential for addressing antimicrobial resistance through more efficient and data-driven therapeutic development strategies.

Quantum-Driven High-Throughput Docking: Advances in Molecular Scoring and Drug Discovery.

Recent developments in QM-based molecular docking and HTD are reviewed, focusing on cases where QM is used explicitly in docking workflows.

Integrative strategies in herbal product-based Drug discovery: from computational models to molecular insights

Conventional drug discovery process is associated with high costs, lengthy development timelines, and high failure rates mainly for complex multifactorial diseases. These challenges highlight the existing need for various innovative discovery strategies. Natural product continues to play an important role in discovery of drugs, with natural compounds and their derivatives accounting for approximately one-third of all small-molecule drugs that were approved from 1981 to 2019. Currently, computational and systems-based approaches are being increasingly incorporated in various steps of herbal-drug discovery. These approaches include molecular docking, virtual screening, quantitative structure-activity relationship (QSAR) modelling, network pharmacology, and bioactivity prediction using machine learning. Other emerging technologies such as genome editing and synthetic biology also provide additional opportunities to access and optimize valuable natural-product metabolites. Together, these tools can aid in identifying candidate molecules, elucidate potential mechanisms, and formulation development, although all of it requires experimental validation. Herbal medicines can modulate multiple molecular targets, hence can be advantageous for complex diseases. Analytical techniques such as hyphenated spectroscopy and metabolomics can further contribute to standardization and quality control. Nanocarrier systems and pharmacogenomic approaches can increase bioavailability and support more personalized therapeutic strategies. This review highlights integrated approaches for improving discovery, validation, and herbal therapeutic development. The combination of traditional knowledge with computational and analytical approaches provides a practical framework for herbal product-based drug discovery. Continued progress will depend on standardization, reproducible methodologies, and robust clinical evidence.