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Data-Driven Drug Discovery and Molecular Informatics

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  • Currently displaying 21 - 40 of 575 publications
Using Generative Modeling to Endow with Potency Initially Inert Compounds with Good Bioavailability and Low Toxicity.
RI Horne, J Wilson-Godber, A González Díaz, ZF Brotzakis, S Seal, RC Gregory, A Possenti, S Chia, M Vendruscolo
– J Chem Inf Model
(2024)
64,
590
(doi: 10.1021/acs.jcim.3c01777)
From pixels to phenotypes: Integrating image-based profiling with cell health data as BioMorph features improves interpretability
S Seal, J Carreras-Puigvert, S Singh, AE Carpenter, O Spjuth, A Bender
– 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
B Baillif, J Cole, I Giangreco, P McCabe, A Bender
– Journal of Cheminformatics
(2023)
15,
124
(doi: 10.1186/s13321-023-00794-w)
Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules.
E Gelžinytė, M Öeren, MD Segall, G Csányi
– 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
E Gelžinytė, M Öeren, MD Segall, G Csányi
(2023)
(doi: 10.26434/chemrxiv-2023-l85nf-v2)
On the difficulty of validating molecular generative models realistically: a case study on public and proprietary data
K Handa, MC Thomas, M Kageyama, T Iijima, A Bender
– Journal of Cheminformatics
(2023)
15,
112
(doi: 10.1186/s13321-023-00781-1)
ACEpotentials.jl: A Julia implementation of the atomic cluster expansion.
WC Witt, C van der Oord, E Gelžinytė, T Järvinen, A Ross, JP Darby, CH Ho, WJ Baldwin, M Sachs, J Kermode, N Bernstein, G Csányi, C Ortner
– 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.
S Seal, O Spjuth, L Hosseini-Gerami, M García-Ortegón, S Singh, A Bender, AE Carpenter
(2023)
(doi: 10.1101/2023.10.15.562398)
Machine Learning Interatomic Potentials to Predict Bond Dissociation Energies
E Gelzinyte
(2023)
(doi: 10.17863/CAM.104854)
MAVEN: compound mechanism of action analysis and visualisation using transcriptomics and compound structure data in R/Shiny.
L Hosseini-Gerami, R Hernansaiz Ballesteros, A Liu, H Broughton, DA Collier, A Bender
– BMC Bioinformatics
(2023)
24,
344
(doi: 10.1186/s12859-023-05416-8)
ACEpotentials.jl: A Julia Implementation of the Atomic Cluster Expansion
WC Witt, CVD Oord, E Gelžinytė, T Järvinen, A Ross, JP Darby, CH Ho, WJ Baldwin, M Sachs, J Kermode, N Bernstein, G Csányi, C Ortner
(2023)
(doi: 10.48550/arxiv.2309.03161)
(link to publication)
Editorial overview: Artificial intelligence (AI) methodology in structural biology.
A Bender, C de Graaf, NM O'Boyle
– Current opinion in structural biology
(2023)
82,
102676
(doi: 10.1016/j.sbi.2023.102676)
wfl Python toolkit for creating machine learning interatomic potentials and related atomistic simulation workflows
E Gelžinytė, S Wengert, TK Stenczel, HH Heenen, K Reuter, G Csányi, N Bernstein
– The Journal of Chemical Physics
(2023)
159,
124801
(doi: 10.1063/5.0156845)
Improving de novo molecule generation for structure-based drug design
M Thomas
(2023)
(doi: 10.17863/CAM.107998)
From Pixels to Phenotypes: Integrating Image-Based Profiling with Cell Health Data Improves Interpretability
S Seal, J Carreras-Puigvert, A Carpenter, O Spjuth, A Bender
(2023)
(doi: 10.1101/2023.07.14.549031)
Transferable machine learning interatomic potential for bond dissociation energy prediction of drug-like molecule
E Gelžinytė, M Öeren, MD Segall, G Csányi
(2023)
(doi: 10.26434/chemrxiv-2023-l85nf)
wfl Python Toolkit for Creating Machine Learning Interatomic Potentials and Related Atomistic Simulation Workflows
E Gelžinytė, S Wengert, TK Stenczel, HH Heenen, K Reuter, G Csányi, N Bernstein
(2023)
(doi: 10.48550/arxiv.2306.11421)
Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data.
S Seal, H Yang, M-A Trapotsi, S Singh, J Carreras-Puigvert, O Spjuth, A Bender
– J Cheminform
(2023)
15,
56
(doi: 10.1186/s13321-023-00723-x)
Explaining Blood–Brain Barrier Permeability of Small Molecules by Integrated Analysis of Different Transport Mechanisms
FMG Cornelissen, G Markert, G Deutsch, M Antonara, N Faaij, I Bartelink, D Noske, WP Vandertop, A Bender, BA Westerman
– J Med Chem
(2023)
66,
7253
(doi: 10.1021/acs.jmedchem.2c01824)
Prediction of Compound Plasma Concentration-Time Profiles in Mice Using Random Forest.
K Handa, P Wright, S Yoshimura, M Kageyama, T Iijima, A Bender
– Molecular Pharmaceutics
(2023)
20,
3060
(doi: 10.1021/acs.molpharmaceut.3c00071)

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