40 citations · 58 across the 4 of their papers we have counts for
6 papers · 1 filter
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…