Samar Mahmoud

Optibrium (United Kingdom) — United Kingdom

Computer Science · Physical Sciences

6h-index150citations19works0.52yr avg
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Research Topics

Computational Drug Discovery Methods(12), Machine Learning in Materials Science(5), Anomaly Detection Techniques and Applications(3), Video Surveillance and Tracking Methods(2), Pharmacogenetics and Drug Metabolism(2)

Publications19 total

Improving Predictions of Molecular Properties with Graph Featurization and Heterogeneous Ensemble Models
Journal of Chemical Information and Modeling·2025· 1 citedOpen Access
Enhanced Deepfake Detection Using Customized Residual Network Architectures and Cross-Dataset Evaluation
2025
Dynamic Image Representations for Crowd Anomaly Detection using Generative Adversarial Networks
2023· 1 cited
Jammo-VRobot: A Cost-Effective Solution for Social Skills Training Programme for Children with High-Functioning Autism
2023· 1 cited
Prediction of In Vivo Pharmacokinetic Parameters and Time–Exposure Curves in Rats Using Machine Learning from the Chemical Structure
Molecular Pharmaceutics·2022· 70 citedOpen Access
Audio-vestibular affection in rheumatoid arthritis
Minia Journal of Medical Research·2022Open Access
Imputation of sensory properties using deep learning
Journal of Computer-Aided Molecular Design·2021· 14 citedOpen Access
Imputation of Sensory Properties Using Deep Learning
2021Open Access
Deep imputation on large‐scale drug discovery data
Applied AI Letters·2021· 12 citedOpen Access
Deep Imputation on Large-Scale Drug Discovery Data
2021· 2 citedOpen Access
Deep Imputation on Large-Scale Drug Discovery Data
2021Open Access
Abnormal High-Density Crowd Dataset
2020· 2 cited
Imputation Versus Prediction: Applications in Machine Learning for Drug Discovery
Future Drug Discovery·2020· 22 citedOpen Access
Understanding Conditional Associations between ToxCast <i>in Vitro</i> Readouts and the Hepatotoxicity of Compounds Using Rule-Based Methods
Chemical Research in Toxicology·2019· 7 cited
Leveraging heterogeneous data from GHS toxicity annotations, molecular and protein target descriptors and Tox21 assay readouts to predict and rationalise acute toxicity
Journal of Cheminformatics·2019· 12 citedOpen Access
UNDERSTANDING CONDITIONAL MODES OF ACTIONS IN CHEMICAL-INDUCED TOXICITY USING RULE MODELS
Apollo (University of Cambridge)·2019Open Access
MOESM2 of Leveraging heterogeneous data from GHS toxicity annotations, molecular and protein target descriptors and Tox21 assay readouts to predict and rationalise acute toxicity
Figshare·2019Open Access
MOESM3 of Leveraging heterogeneous data from GHS toxicity annotations, molecular and protein target descriptors and Tox21 assay readouts to predict and rationalise acute toxicity
Figshare·2019Open Access
Information-Derived Mechanistic Hypotheses for Structural Cardiotoxicity
Chemical Research in Toxicology·2018· 6 cited

Frequent Co-authors

Matthew Segall(8), Thomas M. Whitehead(7), G. J. Conduit(7), Andreas Bender(6), Benedict Irwin(6), Chad H. G. Allen(3), Lewis Mervin(3), Scott K. Rowland(3), Nigel Greene(2), Dmitriy S. Chekmarev(2), Shyam Vyas(2), Jeff Kattas(2), Tamsin E. Mansley(2), Jack Bikker(2), Fredrik Svensson(2), Azedine Zoufir(2), Avid M. Afzal(2), Yasmine Arafa(2), Maha Abdelmohsen(2), Olga Obrezanova(1)