Job description:
The AI-Driven Drug Discovery Group at the Vall d’Hebron Institute of Oncology (VHIO) in Barcelona, led by Dr. Albert Antolin, is seeking an outstanding Machine Learning Scientist to contribute to the development of next-generation ML methods for small-molecule drug discovery.
This is a strategic and highly collaborative position offering a unique opportunity to help shape emerging machine learning approaches for DNA-Encoded Libraries (DEL), ultra-large chemical spaces and AI-driven small-molecule drug discovery. The candidate will have access to unique large-scale datasets generated through the Structural Genomics Consortium and will contribute to the development of innovative DEL-ML methodologies and translational drug discovery applications while interacting closely with international academic and industry collaborators.
Responsibilities:
- Lead the development of machine learning and deep learning methods for early small-molecule drug discovery, particularly for DNA-Encoded Libraries (DEL), hit discovery, prioritization and hit-to-lead optimization.
- Contribute to the design and lead our participation in DEL-ML benchmarking challenges (such as the 1st Target 2035 DREAM Challenge).
- Apply ML methods in translational drug discovery campaigns in collaboration with experimental scientists.
- Collaborate closely with international academic and industry partners, including pharmaceutical and technology companies.
- Present results at consortium meetings and international scientific conferences.
- Lead the scientific publication of your work and contribute to new grant applications.
- Co-mentor junior researchers and contribute to the establishment of reproducible and scalable ML workflows in the group.
Requirements
Studies Required
- PhD in machine learning, artificial intelligence, cheminformatics, computational chemistry, or a related discipline.
Studies Complementary
- Training in drug discovery, medicinal/computational chemistry.
- Training in deep learning, molecular modelling or large-scale data analysis.
Knowledge Required
- Strong knowledge of machine learning and deep learning methodologies.
- Strong programming skills in Python and experience with modern ML frameworks (e.g. PyTorch, TensorFlow, etc.).
- Good working knowledge of Unix/Linux environments and reproducible computational workflows.
- Excellent command of English, both written and spoken.
Knowledge Complementary
- Knowledge of DNA-Encoded Libraries (DEL) and DEL data analysis.
- Knowledge of hit discovery, virtual screening or other drug discovery workflows.
- Familiarity with chemical foundation models, graph neural networks, transformers, geometric deep learning and/or molecular representation learning.
Experience Required
- Proven track record in machine learning applied to small-molecule drug discovery, chemistry, or related domains, evidenced by peer-reviewed publications and/or impactful software development.
- Experience with high-performance computing environments.
Experience Complementary
- Previous experience in academia-industry collaborative projects or in collaborative multidisciplinary research environments or large-scale projects.
- Experience mentoring students or junior researchers.
- Experience contributing to open-source computational projects or benchmark initiatives.
- Experience applying AI methods and collaborating with medicinal chemists, chemical biologists, or other experimental scientists in prospective drug discovery projects.
- Experience working with chemical representations, ultra-large chemical spaces, or cheminformatics tools.
- Experience working with experimental datasets characterized by measurement uncertainty or noise (e.g. DEL data).
- Experience developing machine learning methods that generalize across targets, chemical series, or experimental datasets.
Skills
- Ability to work independently and drive ambitious scientific projects.
- Strong analytical and problem-solving skills.
- Excellent communication and presentation skills.
- Ability to collaborate effectively within multidisciplinary international teams.
- Strong organizational and project management skills.
- Proactive, creative and solution-oriented mindset.