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Vijil Chenthamarakshan

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14 papers

2 author rows

AAAI Conference 2026 Conference Paper

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

Jiří Navrátil
Jarret Ross
Payel Das
Youssef Mroueh
Samuel C Hoffman
Vijil Chenthamarakshan
Brian Belgodere

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 a generative Chemical Language Model (CLM), while using the molecular similarity to the target as a guide. Our method leverages the contextual representations learned from the CLM itself to estimate the molecular similarity, which is then used to adjust the autoregressive sampling strategy of the CLM. At each step of the decoding process, the method tracks the distance of the current generations from the target and updates the logits to encourage the preservation of similarity in generations. We implement the method using a recently proposed ~47M parameter SMILES-based CLM, GP-MoLFormer, and therefore refer to the method as GP-MoLFormer-Sim, which enables a test-time update of the deep generative policy to reflect the contextual similarity to a set of guide molecules. The method is further integrated into a genetic algorithm (GA) and tested on a set of standard molecular optimization benchmarks involving property optimization, molecular rediscovery, and structure-based drug design. Results show that, GP-MoLFormer-Sim, combined with GA (GP-MoLFormer-Sim+GA) outperforms existing training-free baseline methods, when the oracle remains black-box. The findings in this work are a step forward in understanding and guiding the generative mechanisms of CLMs.

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ICML Conference 2025 Conference Paper

Aligning Protein Conformation Ensemble Generation with Physical Feedback

Jiarui Lu
Xiaoyin Chen
Stephen Zhewen Lu
Aurélie C. Lozano
Vijil Chenthamarakshan
Payel Das
Jian Tang 0005

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 efficient accurate protein structure prediction and conformation sampling by learning distributions over crystallographic structures. However, effectively integrating physical supervision into these data-driven approaches remains challenging, as standard energy-based objectives often lead to intractable optimization. In this paper, we introduce Energy-based Alignment (EBA), a method that aligns generative models with feedback from physical models, efficiently calibrating them to appropriately balance conformational states based on their energy differences. Experimental results on the MD ensemble benchmark demonstrate that EBA achieves state-of-the-art performance in generating high-quality protein ensembles. By improving the physical plausibility of generated structures, our approach enhances model predictions and holds promise for applications in structural biology and drug discovery.

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ICML Conference 2024 Conference Paper

Larimar: Large Language Models with Episodic Memory Control

Payel Das
Subhajit Chaudhury
Elliot Nelson
Igor Melnyk
Sarathkrishna Swaminathan
Sihui Dai
Aurélie C. Lozano
Georgios Kollias

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, one-shot updates of knowledge without the need for computationally expensive re-training or fine-tuning. Experimental results on multiple fact editing benchmarks demonstrate that Larimar attains accuracy comparable to most competitive baselines, even in the challenging sequential editing setup, but also excels in speed—yielding speed-ups of 8-10x depending on the base LLM —as well as flexibility due to the proposed architecture being simple, LLM-agnostic, and hence general. We further provide mechanisms for selective fact forgetting, information leakage prevention, and input context length generalization with Larimar and show their effectiveness. Our code is available at https: //github. com/IBM/larimar.

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NeurIPS Conference 2024 Conference Paper

Multi-Scale Representation Learning for Protein Fitness Prediction

Zuobai Zhang
Pascal Notin
Yining Huang
Aurélie Lozano
Vijil Chenthamarakshan
Debora Marks
Payel Das
Jian Tang

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 structure datasets. While initial protein representation learning studies solely focused on either sequence or structural features, recent hybrid architectures have sought to merge these modalities to harness their respective strengths. However, these sequence-structure models have so far achieved only incremental improvements when compared to the leading sequence-only approaches, highlighting unresolved challenges effectively leveraging these modalities together. Moreover, the function of certain proteins is highly dependent on the granular aspects of their surface topology, which have been overlooked by prior models. To address these limitations, we introduce the Sequence-Structure-Surface Fitness ( S3F ) model — a novel multimodal representation learning framework that integrates protein features across several scales. Our approach combines sequence representations from a protein language model with Geometric Vector Perceptron networks encoding protein backbone and detailed surface topology. The proposed method achieves state-of-the-art fitness prediction on the ProteinGym benchmark encompassing 217 substitution deep mutational scanning assays, and provides insights into the determinants of protein function. Our code is at https: //github. com/DeepGraphLearning/S3F.

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NeurIPS Conference 2023 Conference Paper

Efficient Equivariant Transfer Learning from Pretrained Models

Sourya Basu
Pulkit Katdare
Prasanna Sattigeri
Vijil Chenthamarakshan
Katherine Driggs-Campbell
Payel Das
Lav R. Varshney

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-transformed inputs to obtain equivariant outputs from non-equivariant neural networks. While Kaba et al. (2022) are only concerned with training from scratch, we find that equitune performs poorly on equivariant zero-shot tasks despite good finetuning results. We hypothesize that this is because pretrained models provide better quality features for certain transformations than others and simply averaging them is deleterious. Hence, we propose λ-equitune that averages the features using importance weights, λs. These weights are learned directly from the data using a small neural network, leading to excellent zero-shot and finetuned results that outperform equitune. Further, we prove that λ-equitune is equivariant and a universal approximator of equivariant functions. Additionally, we show that the method of Kaba et al. (2022) used with appropriate loss functions, which we call equizero, also gives excellent zero-shot and finetuned performance. Both equitune and equizero are special cases of λ- equitune. To show the simplicity and generality of our method, we validate on a wide range of diverse applications and models such as 1) image classification using CLIP, 2) deep Q-learning, 3) fairness in natural language generation (NLG), 4) compositional generalization in languages, and 5) image classification using pretrained CNNs such as Resnet and Alexnet.

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AAAI Conference 2023 Conference Paper

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

Sourya Basu
Prasanna Sattigeri
Karthikeyan Natesan Ramamurthy
Vijil Chenthamarakshan
Kush R. Varshney
Lav R. Varshney
Payel Das

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-tuned for different groups to satisfy the needs of various downstream tasks. Equi-tuned models benefit from both group equivariance as an inductive bias and semantic priors from pretrained models. We provide applications of equi-tuning on three different tasks: image classification, compositional generalization in language, and fairness in natural language generation (NLG). We also provide a novel group-theoretic definition for fairness in NLG. The effectiveness of this definition is shown by testing it against a standard empirical method of fairness in NLG. We provide experimental results for equi-tuning using a variety of pretrained models: Alexnet, Resnet, VGG, and Densenet for image classification; RNNs, GRUs, and LSTMs for compositional generalization; and GPT2 for fairness in NLG. We test these models on benchmark datasets across all considered tasks to show the generality and effectiveness of the proposed method.

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NeurIPS Conference 2023 Conference Paper

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

Zuobai Zhang
Minghao Xu
Aurelie C. Lozano
Vijil Chenthamarakshan
Payel Das
Jian Tang

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-evolutionary information and structural characteristics. In this work, inspired by the success of denoising diffusion models in generative tasks, we propose the DiffPreT approach to pre-train a protein encoder by sequence-structure joint diffusion modeling. DiffPreT guides the encoder to recover the native protein sequences and structures from the perturbed ones along the joint diffusion trajectory, which acquires the joint distribution of sequences and structures. Considering the essential protein conformational variations, we enhance DiffPreT by a method called Siamese Diffusion Trajectory Prediction (SiamDiff) to capture the correlation between different conformers of a protein. SiamDiff attains this goal by maximizing the mutual information between representations of diffusion trajectories of structurally-correlated conformers. We study the effectiveness of DiffPreT and SiamDiff on both atom- and residue-level structure-based protein understanding tasks. Experimental results show that the performance of DiffPreT is consistently competitive on all tasks, and SiamDiff achieves new state-of-the-art performance, considering the mean ranks on all tasks. Code will be released upon acceptance.

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ICLR Conference 2023 Conference Paper

Protein Representation Learning by Geometric Structure Pretraining

Zuobai Zhang
Minghao Xu
Arian Rokkum Jamasb
Vijil Chenthamarakshan
Aurélie C. Lozano
Payel Das
Jian Tang 0005

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 data in downstream tasks. Despite the effectiveness of sequence-based approaches, the power of pretraining on known protein structures, which are available in smaller numbers only, has not been explored for protein property prediction, though protein structures are known to be determinants of protein function. In this paper, we propose to pretrain protein representations according to their 3D structures. We first present a simple yet effective encoder to learn the geometric features of a protein. We pretrain the protein graph encoder by leveraging multiview contrastive learning and different self-prediction tasks. Experimental results on both function prediction and fold classification tasks show that our proposed pretraining methods outperform or are on par with the state-of-the-art sequence-based methods, while using much less pretraining data. Our implementation is available at https://github.com/DeepGraphLearning/GearNet.

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ICML Conference 2023 Conference Paper

Reprogramming Pretrained Language Models for Antibody Sequence Infilling

Igor Melnyk
Vijil Chenthamarakshan
Pin-Yu Chen
Payel Das
Amit Dhurandhar
Inkit Padhi
Devleena Das

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 region (CDR), which determines the antigen binding affinity and specificity, creates its own unique challenges. Recent deep learning models have shown impressive results, however the limited number of known antibody sequence/structure pairs frequently leads to degraded performance, particularly lacking diversity in the generated sequences. In our work we address this challenge by leveraging Model Reprogramming (MR), which repurposes pretrained models on a source language to adapt to the tasks that are in a different language and have scarce data - where it may be difficult to train a high-performing model from scratch or effectively fine-tune an existing pre-trained model on the specific task. Specifically, we introduce ReprogBert in which a pretrained English language model is repurposed for protein sequence infilling - thus considers cross-language adaptation using less data. Results on antibody design benchmarks show that our model on low-resourced antibody sequence dataset provides highly diverse CDR sequences, up to more than a two-fold increase of diversity over the baselines, without losing structural integrity and naturalness. The generated sequences also demonstrate enhanced antigen binding specificity and virus neutralization ability. Code is available at https: //github. com/IBM/ReprogBERT

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ICML Conference 2021 Conference Paper

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

Yue Cao 0010
Payel Das
Vijil Chenthamarakshan
Pin-Yu Chen
Igor Melnyk
Yang Shen 0001

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) within a fold. To overcome these challenges, we propose Fold2Seq, a novel transformer-based generative framework for designing protein sequences conditioned on a specific target fold. To model the complex sequence–structure relationship, Fold2Seq jointly learns a sequence embedding using a transformer and a fold embedding from the density of secondary structural elements in 3D voxels. On test sets with single, high-resolution and complete structure inputs for individual folds, our experiments demonstrate improved or comparable performance of Fold2Seq in terms of speed, coverage, and reliability for sequence design, when compared to existing state-of-the-art methods that include data-driven deep generative models and physics-based RosettaDesign. The unique advantages of fold-based Fold2Seq, in comparison to a structure-based deep model and RosettaDesign, become more evident on three additional real-world challenges originating from low-quality, incomplete, or ambiguous input structures. Source code and data are available at https: //github. com/IBM/fold2seq.

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NeurIPS Conference 2020 Conference Paper

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
Benjamin Hoover
Matteo Manica

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 and off-target selectivity. CogMol combines adaptive pre-training of a molecular SMILES Variational Autoencoder (VAE) and an efficient multi-attribute controlled sampling scheme that uses guidance from attribute predictors trained on latent features. To generate novel and optimal drug-like molecules for unseen viral targets, CogMol leverages a protein-molecule binding affinity predictor that is trained using SMILES VAE embeddings and protein sequence embeddings learned unsupervised from a large corpus. We applied the CogMol framework to three SARS-CoV-2 target proteins: main protease, receptor-binding domain of the spike protein, and non-structural protein 9 replicase. The generated candidates are novel at both the molecular and chemical scaffold levels when compared to the training data. CogMol also includes insilico screening for assessing toxicity of parent molecules and their metabolites with a multi-task toxicity classifier, synthetic feasibility with a chemical retrosynthesis predictor, and target structure binding with docking simulations. Docking reveals favorable binding of generated molecules to the target protein structure, where 87--95\% of high affinity molecules showed docking free energy $<$ -6 kcal/mol. When compared to approved drugs, the majority of designed compounds show low predicted parent molecule and metabolite toxicity and high predicted synthetic feasibility. In summary, CogMol can handle multi-constraint design of synthesizable, low-toxic, drug-like molecules with high target specificity and selectivity, even to novel protein target sequences, and does not need target-dependent fine-tuning of the framework or target structure information.

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AAAI Conference 2019 Conference Paper

A Sequential Set Generation Method for Predicting Set-Valued Outputs

Tian Gao
Jie Chen
Vijil Chenthamarakshan
Michael Witbrock

Consider a general machine learning setting where the output is a set of labels or sequences. This output set is unordered and its size varies with the input. Whereas multi-label classification methods seem a natural first resort, they are not readily applicable to set-valued outputs because of the growth rate of the output space; and because conventional sequence generation doesn’t reflect sets’ order-free nature. In this paper, we propose a unified framework—sequential set generation (SSG)—that can handle output sets of labels and sequences. SSG is a meta-algorithm that leverages any probabilistic learning method for label or sequence prediction, but employs a proper regularization such that a new label or sequence is generated repeatedly until the full set is produced. Though SSG is sequential in nature, it does not penalize the ordering of the appearance of the set elements and can be applied to a variety of set output problems, such as a set of classification labels or sequences. We perform experiments with both benchmark and synthetic data sets and demonstrate SSG’s strong performance over baseline methods.

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IJCAI Conference 2011 Conference Paper

Concept Labeling: Building Text Classifiers with Minimal Supervision

Vijil Chenthamarakshan
Prem Melville
Vikas Sindhwani
Richard D. Lawrence

The rapid construction of supervised text classification models is becoming a pervasive need across many modern applications. To reduce human-labeling bottlenecks, many new statistical paradigms (e. g. , active, semi-supervised, transfer and multi-task learning) have been vigorously pursued in recent literature with varying degrees of empirical success. Concurrently, the emergence of Web 2. 0 platforms in the last decade has enabled a world-wide, collaborative human effort to construct a massive ontology of concepts with very rich, detailed and accurate descriptions. In this paper we propose a new framework to extract supervisory information from such ontologies and complement it with a shift in human effort from direct labeling of examples in the domain of interest to the much more efficient identification of concept-class associations. Through empirical studies on text categorization problems using the Wikipedia ontology, we show that this shift allows very high-quality models to be immediately induced at virtually no cost.

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AAAI Conference 2011 Conference Paper

Transfer Latent Semantic Learning: Microblog Mining with Less Supervision

Dan Zhang
Yan Liu
Richard Lawrence
Vijil Chenthamarakshan

The increasing volume of information generated on microblogging sites such as Twitter raises several challenges to traditional text mining techniques. First, most texts from those sites are abbreviated due to the constraints of limited characters in one post; second, the input usually comes in streams of large-volumes. Therefore, it is of signiﬁcant importance to develop effective and efﬁcient representations of abbreviated texts for better ﬁltering and mining. In this paper, we introduce a novel transfer learning approach, namely transfer latent semantic learning, that utilizes a large number of related tagged documents with rich information from other sources (source domain) to help build a robust latent semantic space for the abbreviated texts (target domain). This is achieved by simultaneously minimizing the document reconstruction error and the classiﬁcation error of the labeled examples from the source domain by building a classiﬁer with hinge loss in the latent semantic space. We demonstrate the effectiveness of our method by applying them to the task of classifying and tagging abbreviated texts. Experimental results on both synthetic datasets and real application datasets, including Reuters-21578 and Twitter data, suggest substantial improvements using our approach over existing ones.

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