Results 71 to 80 of about 160,727 (166)

Drug-target binding affinity prediction using message passing neural network and self supervised learning

BMC Genomics, 2023
Background Drug-target binding affinity (DTA) prediction is important for the rapid development of drug discovery. Compared to traditional methods, deep learning methods provide a new way for DTA prediction to achieve good performance without much ...
Leiming Xia, Lei Xu, Shourun Pan, Dongjiang Niu, Beiyi Zhang, Zhen Li   +5 more
doaj   +1 more source

Dipeptide Frequency of Word Frequency and Graph Convolutional Networks for DTA Prediction

Frontiers in Bioengineering and Biotechnology, 2020
Deep learning is an effective method to capture drug-target binding affinity, but low accuracy is still an obstacle to be overcome. Thus, we propose a novel predictor for drug-target binding affinity based on dipeptide frequency of word frequency ...
Xianfang Wang, Xianfang Wang, Yifeng Liu, Fan Lu, Hongfei Li, Peng Gao, Dongqing Wei   +6 more
doaj   +1 more source

Augmenting adaptive immunity: progress and challenges in the quantitative engineering and analysis of adaptive immune receptor repertoires

, 2019
The adaptive immune system is a natural diagnostic and therapeutic. It recognizes threats earlier than clinical symptoms manifest and neutralizes antigen with exquisite specificity.
Abbas, Adams, Adli, Alipanahi, Alpert, AlQuraishi, Altan-Bonnet, Angeletti, Angermueller, Antunes, Arora, Arora, Arora, Azizi, Ba, Bachmann, Bandeira, Baran, Barlow, Bashford-Rogers, Bashor, Bassani-Sternberg, Bassani-Sternberg, Bassani-Sternberg, Bassing, Baumgarth, Ben-Hamo, Benjamin, Benjamin, Bentzen, Bentzen, Bepler, Bethune, Bhat, Birnbaum, Biswas, Blythe, Bolotin, Bongartz, Boyd, Bradbury, Bradley, Breden, Brenner, Briggs, Briney, Britanova, Broere, Brown, Buenrostro, Bulik-Sullivan, Burnett, Busse, Cao, Carey, Carmona, Carreno, Casadevall, Casini, Chaudhary, Chen, Cheow, Cheung, Chevalier, Ching, Christina Fan, Cinelli, Cobey, Cohen, Cohen, Cole, Corcoran, Correia, Corrie, Croote, Cybenko, Daberdaku, Dash, Davies, Davis, Davis, Davis, De Neuter, Deac, DeKosky, DeKosky, Devilder, Devulapally, DeWitt, DeWitt 3rd, DeWitt III, Dey, Ecker, Egorov, Egorov, EL-Manzalawy, EL-Manzalawy, EL-Manzalawy, Elgundi, Elhanati, Elhanati, Elhanati, Elhanati, Elton, Emerson, Emini, Esteva, Eyer, Farlik, Farmer, Farmer, Fellouse, Ferdous, Fine, Fink, Fischer, Fischman, Fishman, Flower, Fowler, Frank, Friedensohn, Galperin, Galson, Gee, Gee, Geering, Gentiluomo, Georgiev, Georgiou, Gidoni, Gielis, Glanville, Glanville, Goddard, Goldstein, Goodfellow, Goodfellow, Gowthaman, Graf, Greaves, Greenbaum, Greenspan, Greenspan, Greenspan, Greiff, Greiff, Greiff, Greiff, Greiff, Griffiths, Grigaityte, Guo, Guo, Hadrup, Halperin, Han, Han, Hartweger, Hashimshony, Hashimshony, Haynes, He, Heather, Heather, Hebditch, Heemskerk, Hellman, Hillman, Hochgerner, Hochreiter, Hochreiter, Hoehn, Hoehn, Holder, Holland, Holzinger, Hoogenboom, Hopp, Hornik, Hou, Howie, Hua, Huang, Imkeller, Irvine, Ishino, Islam, Islam, Jackson, Jackson, Jacobs, Jacobsen, Jacobsen, Jain, Jain, Jaitin, Janin, Jardine, Jardine, Jeanbart, Jerne, Jespersen, Joglekar, Jokinen, June, Jyothi, Kabat, Kanyavuz, Kaplon, Karplus, Keich, Keskin, Khosravi-Maharlooei, Kieke, Kingma, Kisielow, Klambauer, Klebanoff, Konishi, Korn, Kotzias, Kovaltsuk, Kramer, Krawczyk, Krawczyk, Krawczyk, Kringelum, Kunik, Kunik, Kuroda, Lanchantin, Lanzarotti, Lapidoth, Lapidoth, Laplane, Larsen, Lauer, Lavinder, Lavoie, LeCun, Lee, Lee, Legutki, Legutki, Levitt, Li, Li, Li, Li, Liberis, Lindeman, Linette, Liu, Liu, Liu, Liu, Lollier, Longo, Lu, Luo, Luo, Luo, López-Santibáñez-Jácome, Løset, MacBeath, Macosko, Madi, Madi, Mahajan, Manavalan, Manczinger, Mangul, Mangul, Marchalonis, Marcou, Margalit, Marrack, Marrack, Marrack, Martin, Martin, Mascola, Mason, Mason, Mayer, McDaniel, Meng, Merbl, Meysman, Mian, Migalska, Miho, Miho, Minhas, Moffett, Mohan, Mora, Mora, Murphy, Murugan, Muyldermans, Navalkar, Nemazee, Newell, Newell, Newell, Nimrod, North, Oakes, Ohlin, Olson, Ostmeyer, Ostmeyer, Ott, Ott, Padlan, Palme, Parker, Parola, Patterson, Pellequer, Perelson, Perelson, Persson, Peterson, Picelli, Pierce, Pittala, Pogorelyy, Pogorelyy, Pogson, Ponomarenko, Ponomarenko, Pou, Preuer, Priel, Purwada, Purwada, Quang, Radbruch, Ramos-López, Ramsköld, Raybould, Remington, Reshetova, Ribeiro, Richards, Richman, Riley, Rives, Rizzetto, Robert, Robins, Robins, Robinson, Rosati, Rosenberg, Rosenblatt, Rubelt, Rubinstein, Sahin, Saikia, Sandve, Sandve, Sblattero, Schmitt, Schneider, Schneider-Hohendorf, Schroeder, Schumacher, Schwab, Scott, Seet, Segler, Seimetz, Sela-Culang, Sela-Culang, Sela-Culang, Sethna, Sethna, Setliff, Sette, Sewell, Shah, Shalev-Shwartz, Sharon, Shaw, Shokri, Shugay, Shugay, Sidhom, Sidhom, Sikorski, Simonovsky, Simonyan, Sircar, Sircar, Slocombe, Smallwood, Soto, Spiess, Stoeckius, Stone, Strauli, Stubbington, Stubbington, Sundararajan, Swartz, Sylwester, Szegedy, Tague, Talmage, Tang, Taussig, Thakkar, Thomas, Thomas, Tickotsky, Tiller, Tirosh, Tompa, Tonegawa, Townsend, Trepel, Trolle, Turchaninova, Turner, Udyavar, Vamathevan, van der Schoot, Van Regenmortel, Venturi, Verkoczy, Vollmers, Voss, Vázquez Bernat, Wainberg, Walsh, Wang, Wang, Wang, Wang, Wang, Wang, Wang, Wardemann, Wardemann, Wardemann, Watson, Weinstein, Weitzner, Weitzner, Whitehead, Williams, Wine, Wine, Wirth, Woese, Wucherpfennig, Wurch, Xu, Xu, Xu, Yaari, Yaari, Yager, Yang, Younger, Zandian, Zarnitsyna, Zhang, Zhang, Zhao, Zhao, Zhao, Zheng, Zirlik, Zong   +478 more
core   +1 more source

Mathematics at the eve of a historic transition in biology

, 2017
A century ago physicists and mathematicians worked in tandem and established quantum mechanism. Indeed, algebras, partial differential equations, group theory, and functional analysis underpin the foundation of quantum mechanism.
Wei, Guo-Wei
core   +2 more sources

SMFF-DTA: using a sequential multi-feature fusion method with multiple attention mechanisms to predict drug-target binding affinity

BMC Biology
Background Drug-target binding affinity (DTA) prediction can accelerate the drug screening process, and deep learning techniques have been used in all facets of drug research.
Xun Wang, Zhijun Xia, Runqiu Feng, Tongyu Han, Hanyu Wang, Wenqian Yu, Xingguang Wang   +6 more
doaj   +1 more source

Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose. [PDF]

, 2018
We introduce the QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone. The method is closely related to the QMOD approach, substituting a learned scoring field for a pocket ...
Cleves, Ann E, Jain, Ajay N
core  

How Can Network-Pharmacology Contribute to Antiepileptic Drug Development? [PDF]

, 2014
Network-pharmacology is a field of pharmacology emerging from the observation that most clinical drugs have multiple targets, contrasting with the previously dominant magic bullet paradigm which proposed the search of exquisitely selective drugs. What is
Di Ianni, Mauricio Emiliano, Talevi, Alan   +1 more
core   +1 more source

DEDTI versus IEDTI: efficient and predictive models of drug-target interactions

Scientific Reports, 2023
Drug repurposing is an active area of research that aims to decrease the cost and time of drug development. Most of those efforts are primarily concerned with the prediction of drug-target interactions.
Arash Zabihian, Faeze Zakaryapour Sayyad, Seyyed Morteza Hashemi, Reza Shami Tanha, Mohsen Hooshmand, Sajjad Gharaghani   +5 more
doaj   +1 more source

Three-Dimensionally Embedded Graph Convolutional Network (3DGCN) for Molecule Interpretation

, 2019
We present a three-dimensional graph convolutional network (3DGCN), which predicts molecular properties and biochemical activities, based on 3D molecular graph.
Bonchev D., Defferrard M., Duvenaud D. K., Goodfellow I.   +3 more
core   +1 more source

DrugForm-DTA: Towards real-world drug-target binding affinity model

Computational and Structural Biotechnology Journal
Drug-target affinity (DTA) prediction is a fundamental challenge in drug discovery. Computational methods for predicting DTA can greatly assist drug design by narrowing the search space and reducing the number of protein-ligand complexes with low ...
Ivan Khokhlov, Anna Tashchilova, Nikolai Bugaev-Makarovskiy, Olga Glushkova, Vladimir Yudin, Anton Keskinov, Sergey Yudin, Dmitry Svetlichnyy, Veronika Skvortsova   +8 more
doaj   +1 more source