429 citations · 610 across the 12 of their papers we have counts for
13 papers
Protein Representation Learning by Geometric Structure Pretraining
Zuobai Zhang, Minghao Xu, Arian Jamasb +4
Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein function or structure. Existing approaches usually pretrain prote…
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…
Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations
Yair Schiff, Vijil Chenthamarakshan, Samuel Hoffman +2
Deep generative models have emerged as a powerful tool for learning useful molecular representations and designing novel molecules with desired properties, with applications in dru…
Optimizing Molecules using Efficient Queries from Property Evaluations
Samuel Hoffman, Vijil Chenthamarakshan, Kahini Wadhawan +2
Machine learning based methods have shown potential for optimizing existing molecules with more desirable properties, a critical step towards accelerating new chemical discovery. H…
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…
Explaining Chemical Toxicity using Missing Features
Kar Wai Lim, Bhanushee Sharma, Payel Das +2
Chemical toxicity prediction using machine learning is important in drug development to reduce repeated animal and human testing, thus saving cost and time. It is highly recommende…
Learning Implicit Text Generation via Feature Matching
Inkit Padhi, Pierre Dognin, Ke Bai +4
Generative feature matching network (GFMN) is an approach for training implicit generative models for images by performing moment matching on features from pre-trained neural netwo…
Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics
Payel Das, Tom Sercu, Kahini Wadhawan +12
De novo therapeutic design is challenged by a vast chemical repertoire and multiple constraints, e.g., high broad-spectrum potency and low toxicity. We propose CLaSS (Controlled La…
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
Vijil Chenthamarakshan, Payel Das, Samuel C. Hoffman +8
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 Ge…
A Sequential Set Generation Method for Predicting Set-Valued Outputs
Tian Gao, Jie Chen, Vijil Chenthamarakshan +1
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 c…
Fairness GAN
Prasanna Sattigeri, Samuel C. Hoffman, Vijil Chenthamarakshan +1
In this paper, we introduce the Fairness GAN, an approach for generating a dataset that is plausibly similar to a given multimedia dataset, but is more fair with respect to protect…
A Study on Passage Re-ranking in Embedding based Unsupervised Semantic Search
Md Faisal Mahbub Chowdhury, Vijil Chenthamarakshan, Rishav Chakravarti +1
State of the art approaches for (embedding based) unsupervised semantic search exploits either compositional similarity (of a query and a passage) or pair-wise word (or term) simil…
WYSIWYE: An Algebra for Expressing Spatial and Textual Rules for Visual Information Extraction
Vijil Chenthamarakshan, Prasad M Desphande, Raghu Krishnapuram +2
The visual layout of a webpage can provide valuable clues for certain types of Information Extraction (IE) tasks. In traditional rule based IE frameworks, these layout cues are map…