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
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.
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
This review examines how machine learning, deep learning, natural language processing (NLP), and generative modeling are being applied across medicinal chemistry and drug development, and highlights how multimodal data fusion, predictive modeling, and human-AI collaborative frameworks are supporting more informed decisions in rational drug design.
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
How machine learning, deep learning, natural language processing, and related computational methods are being applied across the drug discovery process is reviewed, with particular attention to AlphaFold-based protein structure prediction, AI-supported virtual screening, generative chemistry, retrosynthetic planning, digital pathology, and the use of real-world clinical data.
Yue Peng· International Journal of Bio...· 0 citations