arXiv:2610.23791v1 Announce Type: new Abstract: Computational drug discovery requires agents to compose scientific operations and adapt to changing evidence and constraints. Here we introduce DrugHar...
arXiv:2606. 19245v1 Announce Type: new Abstract: Artificial intelligence (AI) agents promise to accelerate drug discovery by compressing interpretation and decision-making loops, but practical deployment requires trusted evaluation on realistic program decisions.
arXiv:2511.20510v3 Announce Type: replace Abstract: Molecule generation from extremely limited training data is a key challenge in drug discovery. Existing fragment-based methods are more suitable th...
The paper introduces MAGI, an open modular agent that orchestrates molecular‑design tools by setting objectives, launching optimization, interpreting structure–activity relationships, and adjusting strategy. In nine retrospective lead‑optimization campaigns from three pharmaceutical companies, MAGI produced valid molecules via either direct LLM generation or delegation to REINVENT 4; LLM proposals stayed closer to local chemistry and achieved comparable or higher activity with fewer operations, while REINVENT explored broader chemical space. The success of a campaign depended on the accuracy of predictive models rather than the generation route, and a blinded evaluation showed chemists could not distinguish MAGI’s outputs from expert‑generated compounds, though the SAR reasoning was deemed plausible yet incomplete. The study positions MAGI as a coordination layer that can be plugged into existing workflows, but notes that the current ceiling for real projects is set by scorer applicability rather than tool orchestration.
arXiv:2610.16975v1 Announce Type: new Abstract: Drug discovery remains a resource-intensive and time-consuming process, involving billions of dollars and a decade of development to advance a drug to...
arXiv:2607. 08404v1 Announce Type: cross Abstract: Current computational approaches for drug design typically focus on generating molecules conditioned on specific targets or general molecular properties, often neglecting the influence of disease context on target behavior and therapeutic outcomes.
arXiv:2502. 18864v2 Announce Type: replace Abstract: Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation.
arXiv:2610.19920v1 Announce Type: new Abstract: Molecular design is often formulated as an optimization problem over existing molecular spaces. However, molecular optimization and the discovery of st...
Molecular design is often formulated as an optimization problem over existing molecular spaces. However, molecular optimization and the discovery of structurally distinct replacement molecules represe...
arXiv:2507.07426v4 Announce Type: replace Abstract: Recent advances in large language models have demonstrated considerable potential in scientific domains such as drug repositioning. However, their...
SpecOpt is a new molecular design task that optimizes the binding specificity of existing drugs by making constrained structural modifications. The method uses an agentic framework that docks a compound against its intended target and known off‑targets, compares residue‑aware atom‑protein contacts, and feeds the differential interactions to a large language model to propose changes. On a benchmark of 915 compounds, SpecOpt increased the target‑off‑target binding gap for 84.8% of cases while preserving drug‑like properties and structural similarity.
arXiv:2606. 01816v1 Announce Type: cross Abstract: Selecting where to intervene on a protein (i.