2025 · npj Drug Discovery · Nature · added 2026-04-21
Structure-based drug design is rapidly evolving, driven by advances in both physics-based and knowledge-based methods. These computational approaches are increasingly integrated across all stages of d Show more
Structure-based drug design is rapidly evolving, driven by advances in both physics-based and knowledge-based methods. These computational approaches are increasingly integrated across all stages of drug discovery. Despite remarkable progress, challenges remain in achieving accuracy, generalizability, computational efficiency, and chemical synthesizability. In this review, we provide a critical overview of advances, strengths, and limitations of recent methods. We also discuss synergies between the two concepts that hold promises for future advancements towards their practical applicability. Show less
Computational drug discovery is essential for screening
potential treatments and reducing the costs and time associated with
proposing or combining drugs for disease management. Despite the
extensive Show more
Computational drug discovery is essential for screening
potential treatments and reducing the costs and time associated with
proposing or combining drugs for disease management. Despite the
extensive research conducted in this field, it remains an emerging area,
particularly with the advent of machine learning, deep learning, and large
language models (LLMs). This systematic review examines the
integration of machine learning and deep learning techniques in drug
discovery, concentrating on three critical areas: drug−drug interactions
(DDIs), drug-target interactions (DTIs), and adverse drug reactions
(ADRs). The review analyzes over 100 papers published between 2020
and 2025, categorizing the methods into deep learning, machine learning,
graph learning, and hybrid models. It highlights the transformative impact
of natural language processing (NLP) and LLMs in extracting meaningful
insights from biomedical literature and chemical data. Furthermore, this work introduces key databases and data sets widely utilized
in drug discovery. Additionally, this review identifies gaps in the existing research, such as the lack of comprehensive studies that
simultaneously address DDI, DTI, and ADR extraction, and it proposes a more holistic approach to fill these gaps. The paper
concludes by thoroughly evaluating various models, underscoring their performance metrics. Show less
Over the past decade, collective intelligence, i.e., the intelligence that emerges from collective efforts, has transformed complex problem-solving and decision-making. In drug discovery, decision-mak Show more
Over the past decade, collective intelligence, i.e., the intelligence that emerges from collective efforts, has transformed complex problem-solving and decision-making. In drug discovery, decision-making often relies on medicinal chemistry intuition. The present study explores the application of collective intelligence in drug discovery, focusing on lead optimization. Ninety-two Sanofi researchers with diverse expertise participated anonymously in an exercise centered on ADMET-related questions. Their feedback was used to build a collective intelligence agent, which was compared to an artificial intelligence model. The study led to three major conclusions: first, collective intelligence improves decision-making in optimizing ADMET endpoints, compared to individual decisions. Second, collective intelligence outperforms artificial intelligence for all other endpoints but hERG inhibition. Finally, we observe complementarity between collective human and artificial intelligence. Overall, this research highlights the potential of collective intelligence in drug discovery and the importance of a synergistic approach combining human and artificial intelligence in project decision making. Show less