Year: 2026
Type: preprint
Abstract: Commercial drug discovery software has traditionally bundled three sources of value: the interface, the scientific models, and the specialized expertise required to run the models through the interface, thereby translating outputs into design decisions. Large language models, coding agents, and tool-using agents are beginning to unbundle this paradigm. Interfaces, analyses, workflow logic, and substantial portions of scientific software can increasingly be generated to meet the needs of a particular scientist, project, and question, while agents can automate sophisticated workflows that once required specialist users. This shift repositions where competitive advantage is created in computational drug discovery. More intuitive interfaces and faster execution alone do not fix model inaccuracies or broaden the applicability domain. As the underlying tooling becomes increasingly accessible and fluid, strategic differentiation will shift away from familiarity with a particular software package and toward superior preditive algorithms, more generalizable models, proprietary experimental data, accumulated discovery intelligence, and the human discernment needed to frame the right questions and evaluate the answers. Molarium, available at https://molarium.org , provides a case study of this transition in the context of molecular design software. Created during an intensive weekend effort of a tireless drug-discovery scientist and an AI coding agent, this open-source molecular workbench operates exclusively in a standard web browser. It integrates local molecular graphics, editing, conformer generation, energy minimization, molecular dynamics, machine-learned potentials, and protein-structure prediction workflows. Beneath its rapidly adaptable interface, Molarium maintains a versioned molecular state together with user actions, method specifications, provenance, and validation evidence. Decoupling an adaptable interface from an enduring scientific core points toward a broader architecture for generated adaptive software in drug discovery.
Source: ChemRxiv
Authors: Rafal Wiewiora, Woody Sherman
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7212047813