429 citations · 1.3k across the 56 of their papers we have counts for
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Reprogramming Pretrained Language Models for Antibody Sequence Infilling
Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen +4
Antibodies comprise the most versatile class of binding molecules, with numerous applications in biomedicine. Computational design of antibodies involves generating novel and diver…
AlphaFold Distillation for Protein Design
Igor Melnyk, Aurelie Lozano, Payel Das +1
Inverse protein folding, the process of designing sequences that fold into a specific 3D structure, is crucial in bio-engineering and drug discovery. Traditional methods rely on ex…
Biological Sequence Design with GFlowNets
Moksh Jain, Emmanuel Bengio, Alex-Hernandez Garcia +10
Design of de novo biological sequences with desired properties, like protein and DNA sequences, often involves an active loop with several rounds of molecule ideation and expensive…
Benchmarking deep generative models for diverse antibody sequence design
Igor Melnyk, Payel Das, Vijil Chenthamarakshan +1
Computational protein design, i.e. inferring novel and diverse protein sequences consistent with a given structure, remains a major unsolved challenge. Recently, deep generative mo…
Characterizing the Latent Space of Molecular Deep Generative Models with Persistent Homology Metrics
Yair Schiff, Vijil Chenthamarakshan, Karthikeyan Natesan Ramamurthy +1
Deep generative models are increasingly becoming integral parts of the in silico molecule design pipeline and have dual goals of learning the chemical and structural features that…