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- Currently displaying 1 - 20 of 559 publications
PromptSMILES: Prompting for scaffold decoration and fragment linking in chemical language models
(2024)
(doi: 10.26434/chemrxiv-2024-z5jnt)
MolScore: A scoring and evaluation framework for de novo drug design
(2024)
PKSmart: An Open-Source Computational Model to Predict in vivo Pharmacokinetics of Small Molecules
(2024)
(doi: 10.1101/2024.02.02.578658)
Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA Drug-Induced Cardiotoxicity Rank
– Journal of Chemical Information and Modeling
(2024)
64,
1172
(doi: 10.1021/acs.jcim.3c01834)
Using Generative Modeling to Endow with Potency Initially Inert Compounds with Good Bioavailability and Low Toxicity.
– Journal of Chemical Information and Modeling
(2024)
64,
590
(doi: 10.1021/acs.jcim.3c01777)
Improved Early Detection of Drug-Induced Liver Injury by Integrating Predictedin vivoandin vitroData
(2024)
(doi: 10.1101/2024.01.10.575128)
From pixels to phenotypes: Integrating image-based profiling with cell health data as BioMorph features improves interpretability.
– Mol Biol Cell
(2024)
35,
mr2
(doi: 10.1091/mbc.E23-08-0298)
Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations
– J Cheminform
(2023)
15,
124
(doi: 10.1186/s13321-023-00794-w)
Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules.
– Journal of Chemical Theory and Computation
(2023)
20,
164
(doi: 10.1021/acs.jctc.3c00710)
Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules
(2023)
On the difficulty of validating molecular generative models realistically: a case study on public and proprietary data.
– Journal of cheminformatics
(2023)
15,
112
(doi: 10.1186/s13321-023-00781-1)
ACEpotentials.jl: A Julia Implementation of the Atomic Cluster Expansion
– The Journal of chemical physics
(2023)
159,
164101
(doi: 10.1063/5.0158783)
Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA DICTrank.
(2023)
(doi: 10.1101/2023.10.15.562398)
Machine Learning Interatomic Potentials to Predict Bond Dissociation Energies
(2023)
(doi: 10.17863/CAM.104854)
MAVEN: compound mechanism of action analysis and visualisation using transcriptomics and compound structure data in R/Shiny
– BMC bioinformatics
(2023)
24,
344
(doi: 10.1186/s12859-023-05416-8)
ACEpotentials.jl: A Julia Implementation of the Atomic Cluster Expansion
(2023)
(doi: 10.48550/arxiv.2309.03161)
Editorial overview: Artificial intelligence (AI) methodology in structural biology.
– Curr Opin Struct Biol
(2023)
82,
102676
(doi: 10.1016/j.sbi.2023.102676)
wfl Python toolkit for creating machine learning interatomic potentials and related atomistic simulation workflows
– Journal of Chemical Physics
(2023)
159,
124801
(doi: 10.1063/5.0156845)
From Pixels to Phenotypes: Integrating Image-Based Profiling with Cell Health Data Improves Interpretability
(2023)
(doi: 10.1101/2023.07.14.549031)
MolScore: A scoring and evaluation framework for de novo drug design
(2023)
(doi: 10.26434/chemrxiv-2023-c4867)