AI Drug Discovery Company Shuimu Molecule Proposes ChatDD Paradigm: Enabling Expert Experience to Enter the Full AI Workflow Through Dialogue
Incubated by Tsinghua University's Artificial Intelligence for Science (AIR) institute, Shuimu Molecule is the only AI drug discovery company among CCTV’s selected Top Ten Chinese AI Startups. Its core argument is that the key bottleneck in past AI drug discovery was not model predictive capability, but the lack of a continuous collaboration channel between expert experience and algorithms. ChatDD aims to allow pharmaceutical research experts to participate in task planning, execution, and correction through natural language dialogue, while validating platform capabilities via its self-developed pipeline, forming a dual-wheel drive strategy.
Drug development is governed by a widely circulated 'three-ten rule': it takes an average of ten years and over one billion US dollars to develop a new drug, yet the success rate remains below ten percent. When Xing Jie, COO of Shuimu Molecule, cited this figure on a CCTV program, the issue he intended to highlight was more specific than cost alone—how can the decades of experience accumulated by a pharmaceutical research expert be truly utilized by AI?
Nie Zaiqing, Chief Scientist at Shuimu Molecule, believes that the critical barrier in past AI drug discovery projects appeared precisely here. Models can predict structures, generate molecules, and rank candidate results, but researchers may not fully understand the basis for the ranking, nor can they easily feed their concerns back into the model. While computational results can be handed over for experimental validation, there is a lack of a continuous collaboration channel between experts and algorithms. This means that the bottleneck in AI drug discovery is not merely single-point prediction accuracy, but the organizational structure of the entire scientific research workflow.
Consequently, Shuimu Molecule proposed a four-stage division of drug development: TMDD (relying on experimental accumulation and expert judgment), CADD (computer-aided calculation and simulation), AIDD (using AI for property prediction and molecule generation), and ChatDD (where experts participate in task planning, execution, and result correction through natural language dialogue). In the first three generations, expert experience and intuition were difficult to effectively interact with and organically integrate into AI models; ChatDD attempts to let experts pose questions and task objectives, have large models break them down and call tools, and then return the results to the experts. Through multiple rounds of interaction, expert experience can efficiently participate in every step of AI drug discovery.