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Data-Driven Drug Discovery and Molecular Informatics

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  • Currently displaying 1 - 20 of 547 publications
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
(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
– J Chem Phys
(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)
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
– 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
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)
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)
MolScore: A scoring and evaluation framework for de novo drug design
M Thomas, NM O'Boyle, A Bender, C de Graaf
(2023)
(doi: 10.26434/chemrxiv-2023-c4867)
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
– Journal of medicinal chemistry
(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)
Benchmarking causal reasoning algorithms for gene expression-based compound mechanism of action analysis
L Hosseini-Gerami, IA Higgins, DA Collier, E Laing, D Evans, H Broughton, A Bender
– BMC Bioinformatics
(2023)
24,
154
(doi: 10.1186/s12859-023-05277-1)
Deep generative models for 3D molecular structure
B Baillif, J Cole, P McCabe, A Bender
– Current opinion in structural biology
(2023)
80,
102566
(doi: 10.1016/j.sbi.2023.102566)
Using Transcriptomics and Cell Morphology Data in Drug Discovery: The Long Road to Practice
L-L Pruteanu, A Bender
– ACS medicinal chemistry letters
(2023)
14,
386
(doi: 10.1021/acsmedchemlett.3c00015)
Mechanism of action deconvolution of the small-molecule pathological tau aggregation inhibitor Anle138b
L Hosseini-Gerami, E Ficulle, N Humphryes-Kirilov, DC Airey, J Scherschel, S Kananathan, BJ Eastwood, S Bose, DA Collier, E Laing, D Evans, H Broughton, A Bender
– Alzheimers Res Ther
(2023)
15,
52
(doi: 10.1186/s13195-023-01182-0)
Mechanism of action deconvolution of the small-molecule pathological tau aggregation inhibitor Anle138b
L Hosseini-Gerami, E Ficulle, N Humphryes-Kirilov, DC Airey, J Scherschel, S Kananathan, BJ Eastwood, S Bose, DA Collier, E Laing, D Evans, H Broughton, A Bender
– Alzheimer's Research & Therapy
(2023)
15,
52
(doi: 10.1186/s13195-023-01182-0)
Integrating structure-based approaches in generative molecular
M Thomas, A Bender, C de Graaf
– Current Opinion in Structural Biology
(2023)
79,
102559
(doi: 10.1016/j.sbi.2023.102559)

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