Aug 2026· Advances in Pharmacology and Therapeutics Journal· 0 citations
TL;DR
The evolving role of AI in modern drug discovery is discussed while highlighting the importance of explainable algorithms, high-quality biomedical data, real-world evidence, and interdisciplinary collaboration.
Abstract
Artificial intelligence (AI) has rapidly evolved from a computational research tool into a major driver of innovation across the pharmaceutical development pipeline. Advances in deep learning, foundation models, protein structure prediction, and generative molecular design have accelerated target identification, compound optimization, toxicity prediction, and biomarker discovery. These developments have substantially reduced the time required to generate and prioritize therapeutic hypotheses.
Despite this remarkable progress, the translation of computational predictions into clinically effective medicines remains challenging. Drug development continues to be limited by biological complexity, patient heterogeneity, incomplete datasets, and the need for rigorous experimental and clinical validation. AI can improve decision-making, but it cannot replace the biological evidence required for regulatory approval or patient care.
This editorial discusses the evolving role of AI in modern drug discovery while highlighting the importance of explainable algorithms, high-quality biomedical data, real-world evidence, and interdisciplinary collaboration. Rather than viewing AI as a replacement for scientists, clinicians, or pharmacologists, it should be considered a powerful partner that enhances scientific reasoning and accelerates translational research.
The future of pharmaceutical innovation will depend on integrating computational intelligence with experimental pharmacology, clinical medicine, and regulatory science. Responsible implementation—not computational sophistication alone—will determine whether AI ultimately delivers safer, more effective, and more personalized therapies for patients.
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 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
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
Artificial intelligence is revolutionizing drug discovery by accelerating target identification, molecular design, virtual screening, and toxicity prediction, while tackling longstanding challenges like high costs and lengthy timelines in traditional pipelines. This review explores recent AI innovations—such as AlphaFold for protein structure prediction, generative models for de novo drug design, and graph neural networks for drug repurposing—alongside real-world case studies from companies like Exscientia, Insilico Medicine, and BenevolentAI, which have produced clinical candidates like DSP-1181 and rentosertib. Despite these advances, key hurdles persist, including data quality issues, model interpretability, synthetic feasibility for complex molecules, and integration with experimental workflows, underscoring the need for explainable AI, better datasets, and ethical frameworks to bridge research gaps. Looking ahead, hybrid AI-experimental approaches and collaborations between pharma giants and AI startups promise to deliver safer, more personalized therapies faster.
P. Jadhav, R. Pingale, Kanchan Gajanan Gawai et al.· International journal for ad...· 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
Artificial intelligence (AI) is evolving from a predictive tool into a foundational computational infrastructure for mechanism-driven pharmacology, fundamentally reshaping drug discovery. This review examines how this transformation addresses persistent challenges in target validation, including data biases and the need for model interpretability, by integrating network pharmacology with advanced deep learning architectures. Specifically, graph neural networks decipher the complex topology of biological systems and transformer models facilitate the fusion of multimodal data, from genomics to real-world clinical records. Coupled with physics-informed neural networks, this integrated framework operates as a predictive computational microscope. It enables comprehensive in silico simulations that span multiple biological scales, encompassing atomic-level molecular interactions and longitudinal patient trajectories. We demonstrate that this AI-driven paradigm is essential for advancing precision medicine, as it systematically translates vast and heterogeneous datasets into testable mechanistic hypotheses. Consequently, this approach accelerates the development of safer, more effective and patient-specific therapies, by de-risking target validation and elucidating novel therapeutic mechanisms. It directly addresses some of the most pressing inefficiencies in contemporary drug discovery and development, offering a pathway towards more rational and efficient therapeutic innovation.
Xuerui Song, Zhi Chen, Y. An et al.· British Journal of Pharmacol...· 0 citations
