Search Results for author:
Found 21 papers, 4 papers with code
Accelerating Inhibitor Discovery for Multiple SARS-CoV-2 Targets with a Single, Sequence-Guided Deep Generative Framework
The COVID-19 pandemic has highlighted the urgency for developing more efficient molecular discovery pathways.
Accurate Clinical Toxicity Prediction using Multi-task Deep Neural Nets and Contrastive Molecular Explanations
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.
Protein Representation Learning by Geometric Structure Pretraining
Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein function or structure.
Sample-Efficient Generation of Novel Photo-acid Generator Molecules using a Deep Generative Model
Photo-acid generators (PAGs) are compounds that release acids ($H^+$ ions) when exposed to light.
Benchmarking deep generative models for diverse antibody sequence design
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
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?
Various representation learning methods in a supervised setting, including the features extracted using graph neural nets, have emerged for such tasks.
Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations
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.
ProGAE: A Geometric Autoencoder-based Generative Model for Disentangling Protein Dynamics
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
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
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).
Explaining Chemical Toxicity using Missing Features
Chemical toxicity prediction using machine learning is important in drug development to reduce repeated animal and human testing, thus saving cost and time.
Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics
De novo therapeutic design is challenged by a vast chemical repertoire and multiple constraints, e. g., high broad-spectrum potency and low toxicity.
Learning Implicit Text Generation via Feature Matching
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.
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
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
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.
A Sequential Set Generation Method for Predicting Set-Valued Outputs
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.
PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences
Our model learns a rich latent space of the biological peptide context by taking advantage of abundant, unlabeled peptide sequences.
Fairness GAN
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.
A Study on Passage Re-ranking in Embedding based Unsupervised Semantic Search
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).
WYSIWYE: An Algebra for Expressing Spatial and Textual Rules for Visual Information Extraction
In traditional rule based IE frameworks, these layout cues are mapped to rules that operate on the HTML source of the webpages.
