Jul 2026· Medicinal research reviews (Print)· 0 citations· 135 references
Medicine
TL;DR
This review emphasizes the influence of new AI platforms like AlphaFold3, molecular interactions are structurally optimized (MISATO), and ZairaChem on the discovery of oncology drugs and examines how AI reconciles chemical design with pharmacological feasibility.
Abstract
Traditional cancer drug discovery encounters challenges, including lengthy synthesis durations, high costs, and a 90% failure rate in clinical trials, primarily due to inadequate chemical design and drug properties. Artificial intelligence (AI) provides powerful computational tools to overcome these issues by speeding up target identification, predicting properties, and optimizing leads. This review emphasizes the influence of new AI platforms like AlphaFold3, molecular interactions are structurally optimized (MISATO), and ZairaChem on the discovery of oncology drugs. We specifically examine how AI reconciles chemical design with pharmacological feasibility. In addition to evaluating these advancements, we meticulously evaluate methodological challenges, including dataset bias, overfitting, insufficient external validation, and reproducibility issues. Furthermore, the development of complex and targeted modalities, such as antibody-drug conjugates (ADCs), aptamer-drug conjugates (Ap‑DCs), and proteolysis-targeting chimeras (PROTACs), is being explored for cancer treatment using AI. Following a detailed review of regulatory and clinical translation issues, this review presents practical tips for improving model validation, data sharing, and incorporation into medicinal chemistry workflows. By examining successes and persistent limitations, this review article offers a strategic roadmap for leveraging AI to provide clinically translatable cancer therapies with enhanced chemical and pharmacological balance.
Artificial intelligence (AI) has emerged as a major technological development influencing modern
pharmaceutical research and development. Machine learning, computational biology, and large-scale
biological data analysis have the potential to improve target identification, molecular optimization, and
clinical development. However, despite growing enthusiasm, significant uncertainty remains regarding
the ability of AI systems to overcome the biological, regulatory, and translational challenges that have
historically limited pharmaceutical innovation. This review examines AI-driven drug discovery from
a biomedical engineering and translational perspective. It evaluates the scientific foundations of AIenabled
pharmaceutical development, including target identification, molecular design, and multimodal
biological modeling, while analyzing key barriers involving biological complexity, clinical translation,
regulatory oversight, and commercialization. Case studies of Recursion Pharmaceuticals and Schrödinger
demonstrate both the opportunities and limitations associated with integrating computational approaches
into therapeutic development. The analysis suggests that AI will become an increasingly important
component of pharmaceutical workflows; however, long-term impact will depend less on algorithmic
advancement alone and more on effective integration with biological validation, experimental rigor,
clinical evidence, and scalable translational infrastructure. AI should therefore be viewed as an enabling
technology that enhances decision-making and prioritization rather than a replacement for traditional
biomedical research processes.
Andrew Matelis· American Journal of Student...· 0 citations
Artificial intelligence (AI) has emerged as one of the most transformative technologies in pharmaceutical research by accelerating drug discovery and medicinal chemistry through machine learning, deep learning, and advanced computational approaches. This review examines the available literature from human clinical studies, computational drug discovery research, systematic reviews, meta-analyses, and clinical investigations, highlighting the applications of AI in target identification, virtual screening, lead optimization, drug repurposing, ADMET prediction, and precision medicine. The review also explores interdisciplinary approaches integrating medicinal chemistry, bioinformatics, structural biology, cheminformatics, and digital healthcare to improve molecular design, reduce research costs, and enhance drug development efficiency. Current evidence indicates that AI significantly improves the accuracy and speed of discovering novel therapeutic compounds while supporting personalized treatment strategies and optimizing clinical trials across diverse therapeutic areas. Despite these advancements, challenges remain regarding data quality, model interpretability, algorithmic bias, regulatory acceptance, and prospective clinical validation. Addressing these limitations through interdisciplinary collaboration and standardized validation frameworks will further strengthen the role of artificial intelligence in advancing drug discovery and medicinal chemistry.
Neha Arora, Yogesh Matta, Monu Kumar et al.· Journal of Pharmaceutical Re...· 0 citations
AutoML mitigates issues by automating key stages of the ML pipeline data preprocessing, model selection, hyperparameter tuning, and evaluation thereby enhancing scalability and reducing dependence on domain expertise.
S. Aydın· ITU Journal of Metallurgy an...· 0 citations
Medicinal chemistry sits at the center of modern drug discovery, yet translating molecular designs into approved medicines remains slow, expensive, and prone to high attrition across the pipeline from target identification to clinical validation. Artificial intelligence (AI) is beginning to reshape this landscape by enabling large-scale integration, interpretation, and generation of chemical, biological, and clinical data for hypothesis generation, chemical space exploration, and iterative cycles of model-guided design and experimental validation. In this review, we examine how machine learning, deep learning, natural language processing (NLP), and generative modeling are being applied across medicinal chemistry and drug development. We outline the principles of major AI modalities and detail their roles in target discovery, virtual screening, molecular property prediction, de novo molecular design, fragment-based optimization, safety and absorption, distribution, metabolism, excretion, and toxicity (ADMET) assessment, and clinical trial design. We highlight how multimodal data fusion, predictive modeling, and human-AI collaborative frameworks are supporting more informed decisions in rational drug design. At the same time, we critically assess the limitations that constrain real-world impact, including data scarcity and inconsistency, model generalizability and interpretability, evolving regulatory expectations, and the persistent gap between in silico predictions and experimentally validated drug candidates. While a small but growing number of AI-guided molecules have entered clinical development, systematic evidence on whether AI-driven approaches ultimately deliver better drugs or faster timelines than traditional methods is still accruing. We discuss emerging opportunities at the intersection of AI with automation, robotics, multimodal biology, protein structure prediction, and autonomous discovery. With rigorous validation, high-quality datasets, and appropriate regulatory frameworks, AI can become a dependable tool for discovering safer, more effective, and more personalized medicines.
Kaicheng U, Sophia Meixuan Zhang, Ziyu Yu et al.· Chemical Society Reviews· 0 citations
This review highlights the synergy between AI and HTS, emphasizing DL techniques such as convolutional neural networks for bioactivity prediction, recurrent neural networks for de novo design, and reinforcement learning for property optimization.
K. Herbetko, Katarzyna Herbetko, Magdalena Mikołajek et al.· Future Medicinal Chemistry· 0 citations
Traditional pharmaceutical R&D is constrained by substantial financial investment, lengthy development cycles, and a high probability of failure. Artificial intelligence (AI) is now being incorporated into multiple stages of the pharmaceutical pipeline, including target identification, molecular design, synthesis planning, and clinical research. This paper reviews how machine learning, deep learning, natural language processing, and related computational methods are being applied across the drug discovery process. Particular attention is given to AlphaFold-based protein structure prediction, AI-supported virtual screening, generative chemistry, retrosynthetic planning, digital pathology, and the use of real-world clinical data. The review also considers limitations that are often hidden by strong computational performance, such as incomplete training data, limited interpretability, weak interoperability, uncertain external validity, and the continuing need for laboratory and clinical confirmation. In addition, several practical examples from industry and academic research are discussed to connect technical principles with their actual use in pharmaceutical development. Future progress is likely to depend on multimodal data integration, explainable models, robotic design-make-test-analyze cycles, privacy-preserving collaboration, and regulatory frameworks that evaluate both model performance and the quality of the evidence generated. AI should therefore be understood as an augmentation technology: it can prioritize hypotheses and accelerate iteration, but it cannot replace biological reasoning, experimental judgment, or clinical responsibility.
Yue Peng· International Journal of Bio...· 0 citations