Results 91 to 100 of about 160,727 (166)

Drug-target interaction/affinity prediction: Deep learning models and advances review

Computers in Biology and Medicine
64 pages, 7 figures, 10 ...
Ali Vefghi, Zahed Rahmati, Mohammad Akbari   +2 more
openaire   +2 more sources

DTA Atlas: A massive-scale drug repurposing database

Artificial Intelligence in the Life Sciences
The drug development process is costly and time-consuming. Repurposing existing approved drugs, an efficient and cost-effective strategy, involves assessing numerous drug-protein pairs to uncover new interactions.
Madina Sultanova, Elizaveta Vinogradova, Alisher Amantay, Ferdinand Molnár, Siamac Fazli   +4 more
doaj   +1 more source

11th German Conference on Chemoinformatics (GCC 2015) : Fulda, Germany. 8-10 November 2015. [PDF]

, 2016
Abel, R, Achenbach, J, Adikwu, UM, Ain, QU, Al-Yamori, R, Alhalabi, Z, Aniceto, N, Ansideri, F, Baker, D, Balducci, A, Banting, L, Barilla, J, Barrett, I, Basu, D, Baumann, K, Bender, A, Bender, A, Bender, A, Berg, E, Bergström, F, Bermudez, M, Bietz, S, Bietz, S, Bodnarchuk, MS, Boeckler, FM, Boeckler, FM, Bojarski, AJ, Bojarski, AJ, Borbulevych, OY, Buchholz, M, Bulusu, KC, Bureau, R, Böckler, FM, Böttcher, S, Büttner, FM, Cao, Q, Cappel, D, Cheeseright, T, Clark, RD, Clark, T, Da Costa, FB, Dahlgren, M, De Graaf, C, Demuth, H-U, Dorfman, R, Dubrucq, K, Ecker, GF, Edman, K, Egelkraut-Holtus, M, Eid, S, Eigner-Pitto, V, Engel, J, Engkvist, O, Epple, M, Essex, JW, Evers, A, Exner, TE, Fan, T-P, Fechner, U, Finkelmann, AR, Firaha, DS, Firth, M, Fourches, D, Fraaije, JH, Frach, R, Frach, R, Fraczkiewicz, R, Freitas, A, Friedrich, N-O, Friesner, R, Fu, X, Fuchs, JE, Fulle, S, Furtado, F, Garg, P, Gervasio, FL, Ghafourian, T, Glen, R, Gracia, RS, Grebner, C, Guallar, V, Göller, AH, Günther, MB, Günther, S, Güssregen, S, Haensele, E, Heidrich, J, Heil, J, Hennig, S, Herrmann, G, Hessler, G, Hilbig, M, Himmler, H-J, Hoffgaard, F, Hogner, A, Hollóczki, O, Horinek, D, Hošek, P, Husch, T, Ibezim, A, Ihlenfeldt, WD, Ihlenfeldt, WD, Jardin, C, Judson, P, Jäger, C, Kalinowski, L, Kalliokoski, T, Kast, SM, Kast, SM, Kast, SM, Kibies, P, Kibies, P, Kirchmair, J, Kirchner, B, Kireeva, N, Klute, W, Koch, O, Koch, P, Kohlbacher, O, Kolb, P, Korth, M, Kos, A, Kramer, C, Krilov, G, Krotzky, T, Krotzky, T, Kuhn, H, Kuhn, MA, Kurczab, R, Kühne, R, Lange, A, Lange, A, Lanig, H, Laufer, S, Levine, Z, Li, X, Lifongo, LL, Lin, T, Lisurek, M, Lokajíček, MV, Mackey, M, Masek, BB, Mathea, M, Matter, H, Mbah, CJ, Mbaze, LM, McWilliams, L, Mervin, L, Mervin, LH, Mittal, S, Mohamad-Zobir, SZ, Montanari, F, Moser, D, Mrugalla, F, Mullen, R, Murray, DC, Nagy, S, Nahum, O, Naß, A, Nguyen, QD, Nogueira, MS, Ntie-Kang, F, Ntie-Kang, F, Ntie-Kang, F, Nwodo, NJ, Oliveira Santos, JS-D, Oliveira, TB, Omoto, K, Onlia, I, Ostroumov, D, Owen, RM, Panecka, J, Patel, H, Pervov, VS, Petrov, A, Pisaková, H, Pleik, S, Polokoff, M, Pongratz, T, Pretzel, J, Proschak, E, Pryde, DC, Pöhner, IA, Rarey, M, Rarey, M, Rarey, M, Rauh, D, Renner, G, Renner, G, Richmond, NJ, Rickmeyer, T, Rippmann, F, Ross, GA, Ruff, M, Rupp, B, Saladino, G, Saleh, N, Sandmann, A, Sandmann, A, Schall, C, Schmidt, D, Schmidt, TC, Schmidt, TJ, Schmidtke, P, Schneider, G, Schomburg, KT, Schram, J, Schulz, R, Schütter, C, Segler, MHS, Senderowitz, H, Shaikh, N, Shea, J-E, Sherman, W, Sievers-Engler, A, Simoben, CV, Simr, P, Sippl, W, Smith, S, Solovev, VP, Soltanshahi, F, Sommer, K, Sotriffer, CA, Spiwok, V, Stehle, T, Steinbrecher, TB, Steudle, A, Sticht, H, Strohfeldt, S, Sánchez-García, E, Tautermann, CS, Torda, AE, Torella, R, Truszkowski, A, Turk, S, Tyrchan, C, Tyrchan, C, Ulander, J, Ulander, J, Van den Broek, K, Van den Broek, K, Van Oeyen, A, Volkamer, A, Wade, RC, Waldman, M, Waller, MP, Wang, L, Warszycki, D, Weber, J, Wessjohann, L, Westerhoff, LM, Whitley, DC, Wieczorek, V, Wolber, G, Yosipof, A, Zdrazil, B, Zielesny, A, Zimmermann, MO, Zoufir, A, Śmieja, M   +259 more
core   +1 more source

BASE: a web service for providing compound-protein binding affinity prediction datasets with reduced similarity bias

BMC Bioinformatics
Background Deep learning-based drug-target affinity (DTA) prediction methods have shown impressive performance, despite a high number of training parameters relative to the available data. Previous studies have highlighted the presence of dataset bias by
Hyojin Son, Sechan Lee, Jaeuk Kim, Haangik Park, Myeong-Ha Hwang, Gwan-Su Yi   +5 more
doaj   +1 more source

Mitigating cold-start problems in drug-target affinity prediction with interaction knowledge transferring. [PDF]

Brief Bioinform, 2022
Nguyen TM, Nguyen T, Tran T.
europepmc   +1 more source

A multimodal DTA prediction method based on triple-view contrastive learning

Complex & Intelligent Systems
Drug-target affinity (DTA) prediction is a crucial step in drug discovery, facilitating the acceleration of lead compound screening and drug repurposing, significantly reducing costs and shortening new drug development timelines.
Xiaoxing Pang, Zhixian Liu, Qingfeng Chen, Wei Lan, Shunhan Yao, Jiahai Liang   +5 more
doaj   +1 more source

3DProtDTA: a deep learning model for drug-target affinity prediction based on residue-level protein graphs. [PDF]

RSC Adv, 2023
Voitsitskyi T, Stratiichuk R, Koleiev I, Popryho L, Ostrovsky Z, Henitsoi P, Khropachov I, Vozniak V, Zhytar R, Nechepurenko D, Yesylevskyy S, Nafiiev A, Starosyla S.   +12 more
europepmc   +1 more source

Impact of Protein Representations on Drug-Target Affinity Prediction

and target proteins can significantly hasten the drug discovery and development process. Utilizing artificial intelligence (AI) models to predict drug-target affinity (DTA) is an affordable and efficient strategy for sifting out undesirable molecules and identifying promising drug candidates.
Marijan, Matija, Tanasijević, Ivan
openaire   +1 more source

Correction to: Breaking the barriers of data scarcity in drug-target affinity prediction. [PDF]

Brief Bioinform, 2023
europepmc   +1 more source

KANPM-DTA: improving drug-target affinity prediction with Kolmogorov-Arnold networks and pretrained models. [PDF]

Brief Bioinform
Rakib MDYK, Alamin MH, Li J, Mamun SS, Gobena KA, Ren S.   +5 more
europepmc   +1 more source