Artificial intelligence and machine learning for lead-to-candidate decision-making and beyond

D McNair - Annual review of pharmacology and toxicology, 2023 - annualreviews.org
The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research
and development has to date focused on research: target identification; docking-, fragment …

Comprehensive Review of Drug–Drug Interaction Prediction Based on Machine Learning: Current Status, Challenges, and Opportunities

NN Wang, B Zhu, XL Li, S Liu, JY Shi… - Journal of Chemical …, 2023 - ACS Publications
Detecting drug–drug interactions (DDIs) is an essential step in drug development and drug
administration. Given the shortcomings of current experimental methods, the machine …

Automatic skin cancer detection in dermoscopy images by combining convolutional neural networks and texture features

SM Alizadeh, A Mahloojifar - International Journal of Imaging …, 2021 - Wiley Online Library
Melanoma is one of the most dangerous types of skin cancer that its early detection can
save patients' lives. Computer‐aided methods can be used for this early detection with …

StackPDB: predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

Q Zhang, P Liu, X Wang, Y Zhang, Y Han, B Yu - Applied Soft Computing, 2021 - Elsevier
DNA-binding proteins (DBPs) not only play an important role in all aspects of genetic
activities such as DNA replication, recombination, repair, and modification but also are used …

Multidrug representation learning based on pretraining model and molecular graph for drug interaction and combination prediction

S Ren, L Yu, L Gao - Bioinformatics, 2022 - academic.oup.com
Motivation Approaches for the diagnosis and treatment of diseases often adopt the multidrug
therapy method because it can increase the efficacy or reduce the toxic side effects of drugs …

Drug combinations: mathematical modeling and networking methods

V Vakil, W Trappe - Pharmaceutics, 2019 - mdpi.com
Treatments consisting of mixtures of pharmacological agents have been shown to have
superior effects to treatments involving single compounds. Given the vast amount of possible …

CDCDB: A large and continuously updated drug combination database

G Shtar, L Azulay, O Nizri, L Rokach, B Shapira - Scientific data, 2022 - nature.com
In recent years, due to the complementary action of drug combinations over mono-therapy,
the multiple-drugs for multiple-targets paradigm has received increased attention to treat …

Integrating multi-modal deep learning on knowledge graph for the discovery of synergistic drug combinations against infectious diseases

Q Ye, R Xu, D Li, Y Kang, Y Deng, F Zhu, J Chen… - Cell Reports Physical …, 2023 - cell.com
The threat to global health posed by unpredictable infections and increasing antimicrobial
resistance necessitates the urgent development of drug combination therapies (DCBs) for …

Machine Learning in Drug Metabolism Study

K Sinha, J Ghosh, PC Sil - Current Drug Metabolism, 2022 - ingentaconnect.com
Metabolic reactions in the body transform the administered drug into metabolites. These
metabolites exhibit diverse biological activities. Drug metabolism is the major underlying …

Survey of machine learning techniques for prediction of the isoform specificity of cytochrome P450 substrates

Y **ong, Y Qiao, D Kihara, HY Zhang… - Current Drug …, 2019 - ingentaconnect.com
Background: Determination or prediction of the Absorption, Distribution, Metabolism, and
Excretion (ADME) properties of drug candidates and drug-induced toxicity plays crucial roles …