6 citations · 11 across the 6 of their papers we have counts for
6 papers
Peptide-GPT: Generative Design of Peptides using Generative Pre-trained Transformers and Bio-informatic Supervision
Aayush Shah, Chakradhar Guntuboina, Amir Barati Farimani
In recent years, natural language processing (NLP) models have demonstrated remarkable capabilities in various domains beyond traditional text generation. In this work, we introduc…
Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties
Srivathsan Badrinarayanan, Chakradhar Guntuboina, Parisa Mollaei +1
Peptides are essential in biological processes and therapeutics. In this study, we introduce Multi-Peptide, an innovative approach that combines transformer-based language models w…
AlloyBERT: Alloy Property Prediction with Large Language Models
Akshat Chaudhari, Chakradhar Guntuboina, Hongshuo Huang +1
The pursuit of novel alloys tailored to specific requirements poses significant challenges for researchers in the field. This underscores the importance of developing predictive te…
IDP-Bert: Predicting Properties of Intrinsically Disordered Proteins (IDP) Using Large Language Models
Parisa Mollaei, Danush Sadasivam, Chakradhar Guntuboina +1
Intrinsically Disordered Proteins (IDPs) constitute a large and structure-less class of proteins with significant functions. The existence of IDPs challenges the conventional notio…
Catalyst Property Prediction with CatBERTa: Unveiling Feature Exploration Strategies through Large Language Models
Janghoon Ock, Chakradhar Guntuboina, Amir Barati Farimani
Efficient catalyst screening necessitates predictive models for adsorption energy, a key property of reactivity. However, prevailing methods, notably graph neural networks (GNNs),…
PeptideBERT: A Language Model based on Transformers for Peptide Property Prediction
Chakradhar Guntuboina, Adrita Das, Parisa Mollaei +2
Recent advances in Language Models have enabled the protein modeling community with a powerful tool since protein sequences can be represented as text. Specifically, by taking adva…