Senior Director, Computational Drug Discovery

Senior Director, Computational Drug Discovery

We are looking for a Senior Director, Computational Drug Discovery to build and lead a small, high‑impact computational drug discovery team and to serve as a peer‑level scientific counterpart to senior computational chemistry and drug discovery leaders at the world’s leading pharmaceutical companies.

At Apheris, we are building the future of how AI is applied in pharmaceutical R&D. Our federated computing technology enables global pharma companies to collaborate securely on large AI models that accelerate drug discovery, without ever sharing their proprietary data. By powering multi‑partner data networks across small molecules, antibodies, and protein folding, Apheris is redefining how science and AI intersect. Apheris is a neutral technology and solutions provider.

We do not conduct internal drug discovery research and do not develop proprietary drug assets. Instead, we support pharmaceutical computational drug discovery teams with production‑grade applications, models, and infrastructure that help advance their own drug programs.

We currently host two flagship networks: the AI Structural Biology (AISB) Network, focused on protein co‑folding and binding affinity prediction, and the ADMET Network, focused on small‑molecule property prediction. In addition, we have just launched Apheris Fold, an enterprise co‑folding solution that enables pharma teams to deploy cutting‑edge models such as Open Fold 3 and Boltz‑2 directly within their own environments.

• Own scientific direction across modalities, data, and workflows
  • Define which drug discovery problems, modalities, and data types Apheris prioritizes with our product offerings.
  • Guide how applications and models should evolve to remain maximally useful to pharma teams.
  • Balance scientific ambition with practicality and adoption in customer environments.
• Build and lead an established, growing computational drug discovery team
  • Lead a core team already in place, including a senior medicinal chemist and a forward‑deployed scientist, both with top‑tier academic and industry backgrounds.
  • Hire and integrate 1–2 additional team members over the next 6–9 months as customer demand scales.
  • Set clear scientific direction and execution standards across all customer‑facing programs.
• Act as the senior scientific counterpart to pharma R&D leaders
  • Engage comfortably and credibly with leading computational drug discovery scientists at large pharmaceutical companies.
  • Lead deep scientific discussions on program strategy, model application, and decision‑making trade‑offs.
  • Provide authoritative guidance grounded in real‑world drug discovery experience.
• Represent Apheris in the scientific ecosystem
  • Build trust and credibility through publications, talks, and direct engagement with the community.
  • Strengthen Apheris’ reputation as a serious, neutral partner for computational drug discovery.
• Drive hands‑on scientific leadership across customer programs
  • Give concrete, actionable scientific direction to application scientists and customer teams.
  • Ensure recommendations reflect how discovery programs are actually run, not theoretical workflows.
  • Review and challenge analyses, models, and conclusions to maintain a high scientific bar.
• Partner closely with Product and ML/AI teams
  • Translate customer needs and scientific insight into clear product and model requirements.
  • Shape prioritization decisions by grounding them in customer impact and discovery realities.
  • Act as a scientific bridge between customers and internal development teams.

• PhD in computational chemistry, cheminformatics, computational drug discovery, or a closely related field.
• 8+ years of post‑PhD industry experience in computational drug discovery or computational chemistry, including proven impact on drug discovery programs from hit‑finding through lead optimization.
• Deep, practical expertise in structure‑based and ligand‑based design, ADMET modeling, and integration of experimental and biophysical data.
• Exceptional understanding of modern ML approaches in drug discovery, with sound judgment on where ML materially improves outcomes.
• Strong…