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Data-Driven Drug Discovery and Molecular Informatics

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  • Currently displaying 1 - 20 of 555 publications
PromptSMILES: Prompting for scaffold decoration and fragment linking in chemical language models
M Thomas, M Ahmad, G Tresadern, G de Fabritiis
(2024)
MolScore: A scoring and evaluation framework for de novo drug design
M Thomas, NM O'Boyle, A Bender, C de Graaf
(2024)
PKSmart: An Open-Source Computational Model to Predict in vivo Pharmacokinetics of Small Molecules
S Seal, M-A Trapotsi, V Subramanian, O Spjuth, N Greene, A Bender
(2024)
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
– J Chem Inf Model
(2024)
64,
1172
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
Improved Early Detection of Drug-Induced Liver Injury by Integrating Predictedin vivoandin vitroData
S Seal, DP Williams, L Hosseini-Gerami, O Spjuth, A Bender
(2024)
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
– Molecular Biology of the Cell
(2024)
35,
mr2
Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations.
B Baillif, J Cole, I Giangreco, P McCabe, A Bender
– J Cheminform
(2023)
15,
124
Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules.
E Gelžinytė, M Öeren, MD Segall, G Csányi
– J Chem Theory Comput
(2023)
20,
164
Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules
E Gelžinytė, M Öeren, MD Segall, G Csányi
(2023)
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
– J Cheminform
(2023)
15,
112
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
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)
Machine Learning Interatomic Potentials to Predict Bond Dissociation Energies
E Gelzinyte
(2023)
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
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)
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
Editorial overview: Artificial intelligence (AI) methodology in structural biology.
A Bender, C de Graaf, NM O'Boyle
– Curr Opin Struct Biol
(2023)
82,
102676
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)
MolScore: A scoring and evaluation framework for de novo drug design
M Thomas, NM O'Boyle, A Bender, C de Graaf
(2023)