Search Results for author: Vijil Chenthamarakshan

Found 21 papers, 4 papers with code

Accurate Clinical Toxicity Prediction using Multi-task Deep Neural Nets and Contrastive Molecular Explanations

1 code implementation13 Apr 2022 Bhanushee Sharma, Vijil Chenthamarakshan, Amit Dhurandhar, Shiranee Pereira, James A. Hendler, Jonathan S. Dordick, Payel Das

Additionally, our multi-task approach is comprehensive in the sense that it is comparable to state-of-the-art approaches for specific endpoints in in vitro, in vivo and clinical platforms.

Transfer Learning

Protein Representation Learning by Geometric Structure Pretraining

no code implementations11 Mar 2022 Zuobai Zhang, Minghao Xu, Arian Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, Jian Tang

Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein function or structure.

Contrastive Learning Representation Learning

Benchmarking deep generative models for diverse antibody sequence design

no code implementations12 Nov 2021 Igor Melnyk, Payel Das, Vijil Chenthamarakshan, Aurelie Lozano

Here we consider three recently proposed deep generative frameworks for protein design: (AR) the sequence-based autoregressive generative model, (GVP) the precise structure-based graph neural network, and Fold2Seq that leverages a fuzzy and scale-free representation of a three-dimensional fold, while enforcing structure-to-sequence (and vice versa) consistency.

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

1 code implementation24 Jun 2021 Yue Cao, Payel Das, Vijil Chenthamarakshan, Pin-Yu Chen, Igor Melnyk, Yang shen

Designing novel protein sequences for a desired 3D topological fold is a fundamental yet non-trivial task in protein engineering.

Do Large Scale Molecular Language Representations Capture Important Structural Information?

no code implementations17 Jun 2021 Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan, Inkit Padhi, Youssef Mroueh, Payel Das

Various representation learning methods in a supervised setting, including the features extracted using graph neural nets, have emerged for such tasks.

Drug Discovery Molecular Property Prediction +1

Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations

no code implementations8 Jun 2021 Yair Schiff, Vijil Chenthamarakshan, Samuel Hoffman, Karthikeyan Natesan Ramamurthy, Payel Das

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.

Drug Discovery Topological Data Analysis

ProGAE: A Geometric Autoencoder-based Generative Model for Disentangling Protein Dynamics

no code implementations1 Jan 2021 Norman Joseph Tatro, Payel Das, Pin-Yu Chen, Vijil Chenthamarakshan, Rongjie Lai

Empowered by the disentangled latent space learning, the extrinsic latent embedding is successfully used for classification or property prediction of different drugs bound to a specific protein.

Optimizing Molecules using Efficient Queries from Property Evaluations

1 code implementation3 Nov 2020 Samuel Hoffman, Vijil Chenthamarakshan, Kahini Wadhawan, Pin-Yu Chen, Payel Das

Machine learning based methods have shown potential for optimizing existing molecules with more desirable properties, a critical step towards accelerating new chemical discovery.

Characterizing the Latent Space of Molecular Deep Generative Models with Persistent Homology Metrics

no code implementations NeurIPS Workshop TDA_and_Beyond 2020 Yair Schiff, Vijil Chenthamarakshan, Karthikeyan Natesan Ramamurthy, Payel Das

In this work, we propose a method for measuring how well the latent space of deep generative models is able to encode structural and chemical features of molecular datasets by correlating latent space metrics with metrics from the field of topological data analysis (TDA).

Topological Data Analysis

Explaining Chemical Toxicity using Missing Features

no code implementations23 Sep 2020 Kar Wai Lim, Bhanushee Sharma, Payel Das, Vijil Chenthamarakshan, Jonathan S. Dordick

Chemical toxicity prediction using machine learning is important in drug development to reduce repeated animal and human testing, thus saving cost and time.

Learning Implicit Text Generation via Feature Matching

no code implementations ACL 2020 Inkit Padhi, Pierre Dognin, Ke Bai, Cicero Nogueira dos santos, Vijil Chenthamarakshan, Youssef Mroueh, Payel Das

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 networks.

Conditional Text Generation Style Transfer +2

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

no code implementations NeurIPS 2020 Vijil Chenthamarakshan, Payel Das, Samuel C. Hoffman, Hendrik Strobelt, Inkit Padhi, Kar Wai Lim, Benjamin Hoover, Matteo Manica, Jannis Born, Teodoro Laino, Aleksandra Mojsilovic

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.

Interactive Visual Exploration of Latent Space (IVELS) for peptide auto-encoder model selection

no code implementations ICLR Workshop DeepGenStruct 2019 Tom Sercu, Sebastian Gehrmann, Hendrik Strobelt, Payel Das, Inkit Padhi, Cicero dos Santos, Kahini Wadhawan, Vijil Chenthamarakshan

We present the pipeline in an interactive visual tool to enable the exploration of the metrics, analysis of the learned latent space, and selection of the best model for a given task.

Model Selection

A Sequential Set Generation Method for Predicting Set-Valued Outputs

no code implementations12 Mar 2019 Tian Gao, Jie Chen, Vijil Chenthamarakshan, Michael Witbrock

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.

General Classification Multi-Label Classification

PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences

no code implementations17 Oct 2018 Payel Das, Kahini Wadhawan, Oscar Chang, Tom Sercu, Cicero dos Santos, Matthew Riemer, Vijil Chenthamarakshan, Inkit Padhi, Aleksandra Mojsilovic

Our model learns a rich latent space of the biological peptide context by taking advantage of abundant, unlabeled peptide sequences.

Fairness GAN

no code implementations24 May 2018 Prasanna Sattigeri, Samuel C. Hoffman, Vijil Chenthamarakshan, Kush R. Varshney

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 protected attributes in allocative decision making.

Decision Making Fairness

A Study on Passage Re-ranking in Embedding based Unsupervised Semantic Search

no code implementations22 Apr 2018 Md. Faisal Mahbub Chowdhury, Vijil Chenthamarakshan, Rishav Chakravarti, Alfio M. Gliozzo

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) similarity (from the query and the passage).

Passage Re-Ranking Re-Ranking +2

WYSIWYE: An Algebra for Expressing Spatial and Textual Rules for Visual Information Extraction

no code implementations28 Jun 2015 Vijil Chenthamarakshan, Prasad M Desphande, Raghu Krishnapuram, Ramakrishna Varadarajan, Knut Stolze

In traditional rule based IE frameworks, these layout cues are mapped to rules that operate on the HTML source of the webpages.

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