AIConfPaper
← Search

Vijil Chenthamarakshan

13 accepted papers

2026

CoFrGeNet: Continued Fraction Architectures for Language Generation

Amit Dhurandhar, Vijil Chenthamarakshan, Dennis Wei, Tejaswini Pedapati, Karthikeyan Natesan Ramamurthy, Rahul Nair
ICML 2026poster

Transformers are arguably the preferred architecture for language generation. In this paper, inspired by continued fractions, we introduce a new function class for generative modeling. The architecture family implementing this function class is named CoFrGeNets - Continued Fraction Generative Networ…

Cited by 0Source ↗Scholar ↗
2026

GP-MoLFormer-Sim: Test Time Molecular Optimization Through Contextual Similarity Guidance

Jiří Navrátil, Jarret Ross, Payel Das, Youssef Mroueh, Samuel C Hoffman, Vijil Chenthamarakshan +1
AAAI 2026technical

The ability to design molecules while preserving similarity to a target molecule and/or property is crucial for various applications in drug discovery, chemical design, and biology. We introduce in this paper an efficient training-free method for navigating and sampling from the molecular space with

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Aligning Protein Conformation Ensemble Generation with Physical Feedback

Jiarui Lu, Xiaoyin Chen, Stephen Zhewen Lu, Aurelie Lozano, Vijil Chenthamarakshan, Payel Das +1
ICML 2025poster

Protein dynamics play a crucial role in protein biological functions and properties, and their traditional study typically relies on time-consuming molecular dynamics (MD) simulations conducted in silico. Recent advances in generative modeling, particularly denoising diffusion models, have enabled e…

Cited by 0Source ↗PDF ↗Scholar ↗
2024

Larimar: Large Language Models with Episodic Memory Control

Payel Das, Subhajit Chaudhury, Elliot Nelson, Igor Melnyk, Sarathkrishna Swaminathan, Sihui Dai +6
ICML 2024poster

Efficient and accurate updating of knowledge stored in Large Language Models (LLMs) is one of the most pressing research challenges today. This paper presents Larimar - a novel, brain-inspired architecture for enhancing LLMs with a distributed episodic memory. Larimar's memory allows for dynamic, on…

Cited by 15Source ↗PDF ↗code ↗Scholar ↗
2024

Multi-Scale Representation Learning for Protein Fitness Prediction

Zuobai Zhang, Pascal Notin, Yining Huang, Aurelie Lozano, Vijil Chenthamarakshan, Debora Susan Marks +2
NeurIPS 2024poster

Designing novel functional proteins crucially depends on accurately modeling their fitness landscape. Given the limited availability of functional annotations from wet-lab experiments, previous methods have primarily relied on self-supervised models trained on vast, unlabeled protein sequence or str…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2023

Efficient Equivariant Transfer Learning from Pretrained Models

Sourya Basu, Pulkit Katdare, Prasanna Sattigeri, Vijil Chenthamarakshan, Katherine Rose Driggs-Campbell, Payel Das +1
NeurIPS 2023poster

Efficient transfer learning algorithms are key to the success of foundation models on diverse downstream tasks even with limited data. Recent works of Basu et al. (2023) and Kaba et al. (2022) propose group averaging (equitune) and optimization-based methods, respectively, over features from group-t…

Cited by 7Source ↗PDF ↗code ↗Scholar ↗
2023

Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models

Sourya Basu, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Vijil Chenthamarakshan, Kush R. Varshney, Lav R. Varshney +1
AAAI 2023technical

We introduce equi-tuning, a novel fine-tuning method that transforms (potentially non-equivariant) pretrained models into group equivariant models while incurring minimum L_2 loss between the feature representations of the pretrained and the equivariant models. Large pretrained models can be equi-tu…

Cited by 27Source ↗PDF ↗Scholar ↗
2023

Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction

Zuobai Zhang, Minghao Xu, Aurelie Lozano, Vijil Chenthamarakshan, Payel Das, Jian Tang
NeurIPS 2023spotlight

Self-supervised pre-training methods on proteins have recently gained attention, with most approaches focusing on either protein sequences or structures, neglecting the exploration of their joint distribution, which is crucial for a comprehensive understanding of protein functions by integrating co-…

Cited by 24Source ↗PDF ↗code ↗Scholar ↗
2023

Protein Representation Learning by Geometric Structure Pretraining

Zuobai Zhang, Minghao Xu, Arian Rokkum Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das +1
ICLR 2023poster

Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein function or structure. Existing approaches usually pretrain protein language models on a large number of unlabeled amino acid sequences and then finetune the models with some labeled da…

Cited by 249Source ↗PDF ↗code ↗Scholar ↗
2023

Reprogramming Pretrained Language Models for Antibody Sequence Infilling

Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit Dhurandhar, Inkit Padhi +1
ICML 2023poster

Antibodies comprise the most versatile class of binding molecules, with numerous applications in biomedicine. Computational design of antibodies involves generating novel and diverse sequences, while maintaining structural consistency. Unique to antibodies, designing the complementarity-determining…

Cited by 29Source ↗PDF ↗code ↗Scholar ↗
2022

Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations

Yair Schiff, Vijil Chenthamarakshan, Samuel C. Hoffman, Karthikeyan Natesan Ramamurthy, Payel Das
ICASSP 2022accepted

Deep generative models have emerged as a powerful tool for learning useful molecular representations and designing novel molecules with desired properties, with applications in drug discovery and material design. However, most existing deep generative models are restricted due to lack of spatial inf…

Cited by 0Source ↗Scholar ↗
2021

Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design

Yue Cao, Payel Das, Vijil Chenthamarakshan, Pin-Yu Chen, Igor Melnyk, Yang Shen
ICML 2021spotlight

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 relationship, as well as the difficulties to capture the diversity of the sequences (therefore structures and functions)…

Cited by 30Source ↗PDF ↗code ↗Scholar ↗
2020

CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models

Vijil Chenthamarakshan, Payel Das, Samuel Hoffman, Hendrik Strobelt, Inkit Padhi, Kar Wai Lim +5
NeurIPS 2020poster

The novel nature of SARS-CoV-2 calls for the development of efficient de novo drug design approaches. In this study, we propose an end-to-end framework, named CogMol (Controlled Generation of Molecules), for designing new drug-like small molecules targeting novel viral proteins with high affinity an…

Cited by 114Source ↗PDF ↗code ↗Scholar ↗