How to approach machine learning-based prediction of drug/compound–target interactions

Autor: Heval Atas Guvenilir, Tunca Doğan

Publikováno v: Journal of Cheminformatics, Vol 15, Iss 1, Pp 1-36 (2023)

Abstract The identification of drug/compound–target interactions (DTIs) constitutes the basis of drug discovery, for which computational predictive approaches have been developed. As a relatively new data-driven paradigm, proteochemometric (PCM) mo

Externí odkaz: https://doaj.org/article/c6682b1be6c2439097c1459f640060f5

Target Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networks

Autor: Ünlü, Atabey, Çevrim, Elif, Sarıgün, Ahmet, Yiğit, Melih Gökay, Çelikbilek, Hayriye, Bayram, Osman, Güvenilir, Heval Ataş, Koyaş, Altay, Kahraman, Deniz Cansen, Olğaç, Abdurrahman, Rifaioğlu, Ahmet, Banoğlu, Erden, Doğan, Tunca

Discovering novel drug candidate molecules is one of the most fundamental and critical steps in drug development. Generative deep learning models, which create synthetic data given a probability distribution, offer a high potential for designing de n

Externí odkaz: http://arxiv.org/abs/2302.07868

Data Centric Molecular Analysis and Evaluation of Hepatocellular Carcinoma Therapeutics Using Machine Intelligence-Based Tools

Autor: Rengul Cetin-Atalay, Deniz Cansen Kahraman, Esra Nalbat, Ahmet Sureyya Rifaioglu, Ahmet Atakan, Ataberk Donmez, Heval Atas, M. Volkan Atalay, Aybar C. Acar, Tunca Doğan

Publikováno v: Journal of Gastrointestinal Cancer. 52:1266-1276

Computational approaches have been used at different stages of drug development with the purpose of decreasing the time and cost of conventional experimental procedures. Lately, techniques mainly developed and applied in the field of artificial intel

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb538c74d54630c9ecfc554e50ef4b31
https://doi.org/10.1007/s12029-021-00768-x

Protein domain-based prediction of drug/compound-target interactions and experimental validation on LIM kinases

Autor: Maria Jesus Martin, Ece Akhan Güzelcan, Marcus Baumann, Ian R. Baxendale, Altay Koyas, Tunca Doğan, Heval Atas, Rengul Cetin-Atalay

Publikováno v: PLoS Computational Biology, Vol 17, Iss 11, p e1009171 (2021)
PLOS Computational Biology, 2021, Vol.17(11), pp.e1009171 [Peer Reviewed Journal]
PLoS Computational Biology

Predictive approaches such as virtual screening have been used in drug discovery with the objective of reducing developmental time and costs. Current machine learning and network-based approaches have issues related to generalization, usability, or m

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::239f268364201e2c929d61db57271896
https://doaj.org/article/300998f38bfd495a97aea9c262e40130

CROssBAR: comprehensive resource of biomedical relations with knowledge graph representations

Autor: Tunca Doğan, Heval Atas, Ahmet Sureyya Rifaioglu, Maria Jesus Martin, Rabie Saidi, Rengul Cetin-Atalay, Hermann Zellner, Volkan Atalay, Vishal Joshi, Andrew Nightingale, Esra Nalbat, Ahmet Atakan, Vladimir Volynkin

Publikováno v: Nucleic Acids Research

Systemic analysis of available large-scale biological/biomedical data is critical for studying biological mechanisms, and developing novel and effective treatment approaches against diseases. However, different layers of the available data are produc

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1ea230a7cafb20eeaf699870685fc865
https://hdl.handle.net/20.500.12508/1859