Molecules to market: A probabilistic artificial intelligence framework for predicting managed care adoption at the level of drug discovery.

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

ABSTRACT Introduction Artificial intelligence (AI) is increasingly proposed as a means of shortening drug-discovery timelines, although its practical impact varies across the discovery and development process. 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. Areas covered This review discusses the use of AI in target and pathway prioritization, variant and protein-structure interpretation, drug repurposing, virtual screening, molecular design, ADMET prediction, biomarker discovery, and patient stratification. The review was informed by iterative searches of PubMed and Google Scholar, supplemented by ResearchGate, covering literature from database inception to 15 July 2026, together with reference lists, clinical-trial registries, regulatory disclosures, company reports, and publicly available pipeline updates. Expert opinion AI is most likely to shorten drug discovery when it improves the quality and sequence of decisions, reduces the number of unnecessary experiments, and identifies failure earlier. Its greatest value will arise when it is integrated with high-quality data, disease-relevant experimental models, expert supervision, and prospective clinical validation.

Predictive Safety in the Era of Artificial Intelligence: Promises, Limits, and the Case for an Enterprise Lifecycle Evidence Ecosystem.

Drug safety remains central to patient benefit, as maximizing the value of beneficial therapies requires recognition, appropriate characterization, and effective mitigation of treatment-related adverse drug reactions (ADRs). These challenges, particularly with the advent of artificial intelligence (AI) tools, have increased interest in predictive safety as a lifecycle scientific capability that seeks to anticipate plausible harm in a timely fashion and translate evolving evidence into more informed decisions across acquisitions, clinical development, and postmarketing use. In this article, predictive safety is used primarily to mean product-level and population- or subgroup-level anticipation of plausible treatment-related harm, rather than an autonomous patient-level clinical decision-making approach. Recent advances in AI, human genetics, mechanistic modeling, translational biomarkers, and real-world data have strengthened the scientific basis for this approach. However, predictive safety should never be viewed as a promise to eliminate ADRs or as a substitute for clinical judgment. Its value would be in improving prospective ADR characterization, supporting portfolio prioritization, and enabling timely and more targeted mitigation. In some settings, notably prospective genotype-based screening before exposure, it might prevent the reaction from occurring. This article argues for the development of a governed AI-supported predictive safety ecosystem. It outlines the reasons predictive safety is needed and the context in which it is most likely to add value. It also discusses principal implementation risks, including fragmented data, unstable phenotypes, model drift, transportability failure, and misuse of probabilistic outputs, together with practical mitigation strategies and leading indicators for early governance response.

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

Can agentic AI dent Eroom's Law? Large reasoning models across the drug discovery and development pipeline.

Productivity in pharmaceutical R&D continues to fall despite deeper biological insight and steady gains in clinical development operations - a phenomenon termed Eroom's Law. Agentic AI workflows powered by reasoning-trained large language models (LLMs), increasingly described as large reasoning models (LRMs), could potentially dent this trend. Unlike earlier task-specific models, these systems couple multi-step reasoning with the ability to plan, invoke external tools and retrieve authoritative information, enabling them to decompose and execute complex scientific and operational tasks. This review discusses agentic AI applications across the drug development continuum, from target discovery to post-market surveillance, and highlights three near-term use cases: algorithmic drug repurposing, informed consent support and automated drafting of regulatory documents. For each, we outline plausible architectures, the current level of supporting evidence and the principal failure modes that constrain deployment. Realizing these gains, however, requires prospective validation, rigorous human oversight and governance frameworks that align algorithmic outputs with clinical, ethical, legal and regulatory standards. When implemented responsibly, agentic AI could transform human-AI collaboration in biopharma, improving R&D efficiency and accelerating delivery of safer, more-effective therapies.

Conformal Prediction for Molecular Properties under Label Shift

This work addresses one of the most pervasive obstacles to applying AI in real-world drug development by addressing conformal prediction framework tailored to label shift by weighting conformal scores using marginal label probability ratios and enhancing the trustworthiness of AI-driven predictions.

Model Validation Protocols for Machine Learning in Small Molecule Drug Discovery

This work presents a model validation framework consisting of five recommendations that would enable the community to move beyond aggregate metrics toward understanding where and why molecular property prediction models fail, and connects evaluation choices to real-world applications and case studies encountered in pharmaceutical research.