[HTML][HTML] Empowering biomedical discovery with AI agents
We envision" AI scientists" as systems capable of skeptical learning and reasoning that
empower biomedical research through collaborative agents that integrate AI models and …
empower biomedical research through collaborative agents that integrate AI models and …
Modern machine‐learning for binding affinity estimation of protein–ligand complexes: Progress, opportunities, and challenges
Abstract Structure‐based drug design is a widely applied approach in the discovery of new
lead compounds for known therapeutic targets. In most structure‐based drug design …
lead compounds for known therapeutic targets. In most structure‐based drug design …
Exploring the Potential of Adaptive, Local Machine Learning in Comparison to the Prediction Performance of Global Models: A Case Study from Bayer's Caco-2 …
Machine learning (ML) techniques are being widely implemented to fill the gap in simple
molecular design guidelines for newer therapeutic modalities in the extended and beyond …
molecular design guidelines for newer therapeutic modalities in the extended and beyond …
ChemSpaceAL: An efficient active learning methodology applied to protein-specific molecular generation
The incredible capabilities of generative artificial intelligence models have inevitably led to
their application in the domain of drug discovery. It is therefore of tremendous interest to …
their application in the domain of drug discovery. It is therefore of tremendous interest to …
Closed-Loop Navigation of a Kinetic Zone Diagram for Redox-Mediated Electrocatalysis Using Bayesian Optimization, a Digital Twin, and Automated Electrochemistry
M Pence, G Hazen, JR López - 2024 - chemrxiv.org
Molecular electrocatalysis campaigns often require tuning multiple experimental parameters
to obtain kinetically insightful electrochemical measurements, a prohibitively time …
to obtain kinetically insightful electrochemical measurements, a prohibitively time …