Artificial intelligence in drug discovery - what it is, where we stand and the path forward.

[Drug Discovery - A surge in innovation driven by technology and artificial intelligence].

Is AI Capable of Real-World Drug Discovery?

Small-molecule drug discovery frequently operates in regimes where slight structural changes have large consequences. These are situations in which current artificial intelligence (AI) methods, trained on mass data, may perform poorly. For a substantial fraction of drug-discovery projects, limited biological understanding and sparse data further constrain progress and define regimes where AI approaches remain difficult to apply. Despite these limitations, the global investment in AI continues to grow rapidly. Using two case studies characterized by structural and biochemical data, this article examines how the close integration of computational and medicinal chemistry coupled with a deep understanding of chemical shape and protein interactions enabled targeted computational molecular design to produce disproportionate gains in efficacy and selectivity. These examples highlight a challenge for current AI methods: sensitivity to subtle, low-data perturbations. For AI to achieve a transformative impact in drug discovery, it must move beyond pattern recognition to understand, or explicitly simulate, the mechanistic “why” linking subtle structural changes to biological outcomes. Pending such advances, clear opportunities for AI to productively complement human efforts, rather than be used as a stand-alone solution, are delineated.

Can Artificial intelligence meaningfully shorten drug discovery timelines? Current evidence, roadblocks and future directions

A critical review is needed to distinguish the stages in which AI can improve search, prioritization, and decision-making from those that remain limited by experimental validation, safety assessment, and clinical evidence generation.

AI in Drug Discovery: Applications, Challenges and Future Prospects

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.

Big Data and Artificial Intelligence in Cancer Drug Discovery: Promise, Challenges, and Emerging Opportunities

Oncologic drug development is lengthy (~14 years) and expensive (~1.2 billion USD) with low clinical trial success rates (4.1%). Big data and artificial intelligence (AI) are widely proposed as tools to address these challenges. In this review, we examine the current performance and future potential of big data and AI applied to preclinical discovery and development, clinical trials, and the regulatory approval process. We first examine the data foundation required for effective AI, including data harmonization, data commons, and analytical tools. We then assess preclinical applications spanning target identification, compound-library curation, virtual ligand screening, generative chemical design, and high-throughput and high-content screening. In clinical development, we consider the use of big data and AI for outcome prediction, trial design, external and synthetic control arms, adaptive monitoring, and in silico trials. Finally, we discuss how post-approval electronic health records can generate real-world data and real-world evidence to support drug repurposing and improve future oncology drug discovery. Big data is conventionally characterized by a series of “Vs.” In this review, we have used seven “Vs” spanning descriptive and constraining properties of big data and a singular outcome. We have proposed an eighth, Vernacular, a constraint defined as the combined alignment of data semantics and terminology, data representation, data exchange, and data governance across heterogeneous, independently generated datasets to promote interoperability and combined analysis. Although cancer data exhibit substantial Volume, Velocity, and Variety, they remain distributed across fragmented repositories that often cannot be readily integrated. We conclude with a discussion of tabulated resources currently available for the application of big data and AI to oncologic therapeutics.

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.