WhiteLab Genomics to Showcase AI-Driven Outcomes Advancing the Future of Genomic Medicine at ASGCT 2025
Whitelab Genomics, a techbio company specializing in artificial intelligence to accelerate the development of genomic medicines, announces the presentation of four research posters at the upcoming 28th Annual Congress of the American Society of Gene & Cell Therapy (ASGCT), taking place May 13-17th, 2025 in New Orleans, USA.
Enhancing AAV Gene Therapy Targeting Retinal Diseases with AI-Guided Rational Design
Poster 1390 - Wednesday, May 14, 5:30PM - 7:00PM
Authors: A.Barberis, B. Dafniet, T. Van Meter, U. Ferrari, P. El Darazi, D. Serillon, D. Dalkara, J. Cottineau
WhiteLab Genomics, Future4Care, 8 rue Jean Antoine de Baïf, 75013 Paris, France
Institut de la Vision, Paris, France
As part of the GEAR Consortium with Institut de la Vision and ADLIN Science, we applied AI-guided rational design to engineer next-generation AAV2 vectors for broader, low-dose retinal transduction by targeting conserved photoreceptor surface receptors. Through integrating single-cell genomics, cross-species validation, and structural biology, we achieved the consortium’s first key milestone: the identification of a highly specific photoreceptor surface receptor as a target for next-generation AAV vector design.
Novel In Silico Generation of a Synthetic Peptide Library Derived from Biological Complexes for Protein and Vector Engineering
Poster 1666 - Thursday, May 15, 5:30PM-7:00PM
Authors: C.Alliot, B.Dafniet, Y.Habtoun, C.Colas, J.Maes, D. DelBourgo, J.Cottineau, D.Serillon
WhiteLab Genomics FUTURE4CARE, 8 rue Jean Antoine de Baif, 75013 Paris, France
Using structurally resolved protein–peptide complex data, we developed a synthetic peptide library to fill a major gap in publicly available resources. By expanding the known chemical space with over 1.2 million unique 3D peptide structures across diverse conformations, our AI-driven, rational-guided approach enhances in silico discovery and significantly improves the design and success rate of viral and non-viral vectors in genomic medicine.
Revolutionizing Peptide Discovery: AI-Driven Drug Design Paving the Way for the Future of Cell & Gene Therapy
Poster 1667 - Thursday, May 15, 5:30PM-7:00PM
Authors: C. Alliot, C. Colas, B. Dafniet, J. Maes, J-P. Buffet, P. Vidal, A. Murza, D. Del Bourgo, J.Cottineau, P-L. Boudreault, D. Serillon
WhiteLab Genomics, FUTURE4CARE, 8 rue Jean Antoine de Baif, 75013 Paris, France
Department of Pharmacology and Physiology, Faculty of Medicine and Health Sciences, Institut de Pharmacologie de Sherbrooke, Université de Sherbrooke, 3001, 12e Avenue Nord, J1H 5N4 Sherbrooke, Québec, Canada
ALFRED, our AI platform, integrates rational design, virtual screening, and structural optimization to identify high-affinity peptides. These peptides can be used to functionalize viral and non-viral vectors for the active targeting of challenging receptor binding sites. By achieving hit rates of 8.3%, with all hits outperforming the reference peptide, our approach demonstrates the potential of AI-enhanced peptide design to accelerate targeted therapeutic delivery and advance next-generation genomic medicine.
A Lentiviral Vector Downstream Processing Analytic Tool Powered by Knowledge Graph and LLM Technology
Poster 1790 - Thursday, May 15, 5:30PM-7:00PM
Authors: Yanis Habtoun, Ajanthan Nesarajah, D. Del Bourgo, Julien Cottineau, Oscar De Felice, Anne Galy
WhiteLab Genomics, Future4Care, 8 rue Jean Antoine de Baïf, 75013 Paris, France
ART-TG, Inserm, 30 rue H. Desbruères, 91100 Corbeil-Essonnes, France
In collaboration with Anne Galy’s team at ART-TG Laboratory, we developed OLIVIA, a tool that leverages Large Language Models, Retrieval-Augmented Generation, and a knowledge graph to structure and analyze heterogeneous lentiviral vector bioproduction data. OLIVIA enables process benchmarking and automated analysis, laying the groundwork for machine learning approaches that could enhance bioproduction performance and quality.
