Too many early drug discovery programs move forward with fragmented evidence: promising biology, uncertain chemistry, incomplete ADME/Tox understanding, and limited translational confidence. Genmap-Pro was created to bring these dimensions together into a single decision framework.
Our approach
We combine computational models, biological interpretation and strategic R&D expertise. Our role is not to replace experiments, but to help teams ask better questions and prioritize the most informative next studies.
In addition to evaluating existing assets, Genmap-Pro can generate AI-designed molecular analogues and rank them through activity, selectivity, ADMET/Tox, Safety Shield 44™ and confidence filters. This allows teams to move from a single molecule or hypothesis toward a broader, prioritized set of experimentally actionable options.
CONFIDENTIAL BY DESIGN
Your science.
Your decisions.
Start with a non-confidential discussion.
Agree NDA arrangements before sharing sensitive project information.
Independence and confidentiality
Genmap-Pro provides an independent, confidential assessment designed to support internal review, board discussions, partnering decisions and due diligence.
Lyon, France · Established in 2026
About Genmap
Genmap-Pro is Genmap’s preclinical decision intelligence platform for biotech, pharmaceutical R&D and investment teams. Based in Lyon, Genmap connects therapeutic biology, molecular assessment, AI-guided design and early risk evaluation.
Scientific expertise with a clear purpose
AI/ML, network biology, cheminformatics and predictive pharmacology support the assessment of small-molecule programs. The goal is to identify the uncertainties that matter and the experiments most likely to resolve them.
Meet the people responsible for your assessment
Discuss the scientific scope, expertise required and review responsibilities before starting a project. Follow our public company profile and published scientific discussions on LinkedIn.
Our philosophy
Genmap-Pro does not treat AI predictions as conclusions. We treat them as structured evidence, interpreted in context, challenged through uncertainty, domain applicability and biological plausibility, then translated into decision-ready recommendations.
