113 citations · 221 across the 19 of their papers we have counts for
3 papers · 1 filter
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…
ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models
Pierre L. Dognin, Inkit Padhi, Igor Melnyk +1
Automatic construction of relevant Knowledge Bases (KBs) from text, and generation of semantically meaningful text from KBs are both long-standing goals in Machine Learning. In thi…
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design
Yue Cao, Payel Das, Vijil Chenthamarakshan +3
Designing novel protein sequences for a desired 3D topological fold is a fundamental yet non-trivial task in protein engineering. Challenges exist due to the complex sequence--fold…