A combination of machine learning and infrequent metadynamics to efficiently predict kinetic rates, transition states, and molecular determinants of drug dissociation from G protei... von Lamim Ribeiro, João Marcelo, Provasi, Davide, Filizola, Marta

Volltext

Machine learning approaches for analyzing and enhancing molecular dynamics simulations von Wang, Yihang, Lamim Ribeiro, João Marcelo, Tiwary, Pratyush

Volltext

Past–future information bottleneck for sampling molecular reaction coordinate simultaneously with thermodynamics and kinetics von Wang, Yihang, Ribeiro, João Marcelo Lamim, Tiwary, Pratyush

Volltext

Reweighted autoencoded variational Bayes for enhanced sampling (RAVE) von Ribeiro, João Marcelo Lamim, Bravo, Pablo, Wang, Yihang, Tiwary, Pratyush

Volltext

Toward Achieving Efficient and Accurate Ligand-Protein Unbinding with Deep Learning and Molecular Dynamics through RAVE von Lamim Ribeiro, João Marcelo, Tiwary, Pratyush

Volltext

Allostery in G protein-coupled receptors investigated by molecular dynamics simulations von Lamim Ribeiro, João Marcelo, Filizola, Marta

Volltext

Kinetics of Ligand–Protein Dissociation from All-Atom Simulations: Are We There Yet? von Ribeiro, João Marcelo Lamim, Tsai, Sun-Ting, Pramanik, Debabrata, Wang, Yihang, Tiwary, Pratyush

Volltext

Insights From Molecular Dynamics Simulations of a Number of G-Protein Coupled Receptor Targets for the Treatment of Pain and Opioid Use Disorders von Ribeiro, João Marcelo Lamim, Filizola, Marta

Volltext

De Novo Design of κ-Opioid Receptor Antagonists Using a Generative Deep-Learning Framework von Salas-Estrada, Leslie, Provasi, Davide, Qiu, Xing, Kaniskan, Husnu Ümit, Huang, Xi-Ping, DiBerto, Jeffrey F, Lamim Ribeiro, João Marcelo, Jin, Jian, Roth, Bryan L, Filizola, Marta

Volltext

Differences in interactions between transmembrane domains tune the activation of metabotropic glutamate receptors von Thibado, Jordana K, Tano, Jean-Yves, Lee, Joon, Salas-Estrada, Leslie, Provasi, Davide, Strauss, Alexa, Marcelo Lamim Ribeiro, Joao, Xiang, Guoqing, Broichhagen, Johannes, Filizola, Marta, Lohse, Martin J, Levitz, Joshua

Volltext

AI-assisted de novo design of selective κ-opioid receptor antagonists for the treatment of opioid addiction von Salas Estrada, Letty Leslie Ann, Provasi, Davide, Lamim Ribeiro, Joao Marcelo, Fiorillo, Bianca, Filizola, Marta

Volltext

Markov State Model Analysis of Dynamic Dimerization Interfaces between Transmembrane Domains of Metabotropic Glutamate Receptors von Marcelo Lamim Ribeiro, Joao, Salas-Estrada, Leslie A., Provasi, Davide, Thibado, Jordana K., Levitz, Josh T., Filizola, Marta

Volltext

De Novo KOR Antagonists Datasets von Salas-Estrada, Leslie, Provasi, Davide, Qui, Xing, Kaniskan, Husnu Ümit, Huang, Xi-Ping, DiBerto, Jeffrey F., Lamim Ribeiro, João Marcelo, Jin, Jian, Roth, Bryan L., Filizola, Marta

Volltext bestellen

Machine learning approaches for analyzing and enhancing molecular dynamics simulations von Wang, Yihang, Ribeiro, Joao Marcelo Lamim, Tiwary, Pratyush

Volltext bestellen

Ligand dissociation mechanisms from all-atom simulations: Are we there yet? von Ribeiro, Joao Marcelo Lamim, Tsai, Sun-Ting, Pramanik, Debabrata, Wang, Yihang, Tiwary, Pratyush

Volltext bestellen

Reweighted Autoencoded Variational Bayes for Enhanced Sampling (RAVE) von Ribeiro, Joao Marcelo Lamim, Collado, Pablo Bravo, Wang, Yihang, Tiwary, Pratyush

Volltext bestellen

Machine learning approaches for analyzing and enhancing molecular dynamics simulations von Wang, Yihang, Joao Marcelo Lamim Ribeiro, Tiwary, Pratyush

Volltext

Ligand dissociation mechanisms from all-atom simulations: Are we there yet? von Joao Marcelo Lamim Ribeiro, Sun-Ting, Tsai, Pramanik, Debabrata, Wang, Yihang, Tiwary, Pratyush

Volltext

Reweighted Autoencoded Variational Bayes for Enhanced Sampling (RAVE) von Joao Marcelo Lamim Ribeiro, Pablo Bravo Collado, Wang, Yihang, Tiwary, Pratyush

Volltext