Luca Gambini
Principal Scientist Discovery Chemistry Johnson & Johnson
Luca Gambini is a Principal Research Scientist with expertise spanning medicinal chemistry, peptide drug discovery, and computational approaches to pharmaceutical innovation. With a strong foundation in peptide and organic chemistry, he has contributed to the development of novel therapeutics across multiple disease areas, including oncology and infectious diseases, from early discovery through preclinical development.
Luca combines extensive hands-on experience in solid-phase peptide synthesis, hit-to-lead optimization, and bioactive molecule design with a growing specialization in molecular modeling, computational chemistry, and machine learning-driven drug discovery. His technical expertise encompasses a broad range of analytical and drug discovery techniques, including LC-MS purification, NMR analysis, protein thermal shift assays, and ITC analysis, alongside advanced computational tools for docking, molecular dynamics simulations, and binding site analysis.
As a scientific leader, Luca has successfully supervised and managed multidisciplinary medicinal chemistry teams, driving research programs from concept to candidate selection while fostering innovation at the interface of chemistry and computational science.
Seminars
Are your AI-driven discovery workflows accelerating innovation, or simply generating more noise?
With AI rapidly transforming peptide discovery, how can you separate genuinely impactful technologies from overhyped solutions? Which computational approaches are delivering measurable improvements in hit identification, optimisation and candidate selection? And how can researchers overcome the limitations of current AI models when designing peptides with non-canonical amino acids and increasingly complex modalities?
This workshop will gather experts to discuss:
- Distinguish between AI tools that deliver real value versus overhyped solutions that waste resources and time
- Master co-folding methods like AlphaFold for identifying binders against challenging targets with non-canonical amino acids
- Learn how molecular dynamics simulations can test and refine co-folding predictions by capturing how peptides behave in motion
- Learn how to combine AI predictions, molecular dynamics simulations and experimental data generation with informed model training for superior hit discovery
- Discover which AI-driven design approaches work for macrocyclic peptides versus structured mini-binders.
- Standardising non‑canonical amino acids, a scaffold‑driven monomer nomenclature and a centralised, harmonised database (>7000 standardised monomers) provide a consistent, machine‑readable representation that links chemical structures and sequence notation.
- Integrating standardisation across the DMTA cycle, governed submission‑and‑review workflows and cross‑platform connections (design, registration, and analysis tools) embed consistent monomer representation into routine peptide work, reducing ambiguity and improving data quality for decision‑making.
- Supporting computer‑aided and AI‑based design, a structured, high‑quality data foundation provides the input required for machine learning and computer‑aided design, and improves cross‑functional consistency in peptide programs, contributing to more efficient peptide optimisation.
