Zobrazeno 1 - 10
of 320
pro vyhledávání: '"Engkvist, Ola"'
This study investigates the risks of exposing confidential chemical structures when machine learning models trained on these structures are made publicly available. We use membership inference attacks, a common method to assess privacy that is largel
Externí odkaz:
http://arxiv.org/abs/2410.16975
Fine-tuning a pre-trained generative model has demonstrated good performance in generating promising drug molecules. The fine-tuning task is often formulated as a reinforcement learning problem, where previous methods efficiently learn to optimize a
Externí odkaz:
http://arxiv.org/abs/2410.10431
Accurate prediction of thermodynamic properties is essential in drug discovery and materials science. Molecular dynamics (MD) simulations provide a principled approach to this task, yet they typically rely on prohibitively long sequential simulations
Externí odkaz:
http://arxiv.org/abs/2410.10605
Autor:
Geylan, Gökçe, Janet, Jon Paul, Tibo, Alessandro, He, Jiazhen, Patronov, Atanas, Kabeshov, Mikhail, David, Florian, Czechtizky, Werngard, Engkvist, Ola, De Maria, Leonardo
Peptides play a crucial role in the drug design and discovery whether as a therapeutic modality or a delivery agent. Non-natural amino acids (NNAAs) have been used to enhance the peptide properties from binding affinity, plasma stability to permeabil
Externí odkaz:
http://arxiv.org/abs/2409.14040
Autor:
Svensson, Emma, Friesacher, Hannah Rosa, Winiwarter, Susanne, Mervin, Lewis, Arany, Adam, Engkvist, Ola
In the early stages of drug discovery, decisions regarding which experiments to pursue can be influenced by computational models. These decisions are critical due to the time-consuming and expensive nature of the experiments. Therefore, it is becomin
Externí odkaz:
http://arxiv.org/abs/2409.04313
In the drug discovery process, where experiments can be costly and time-consuming, computational models that predict drug-target interactions are valuable tools to accelerate the development of new therapeutic agents. Estimating the uncertainty inher
Externí odkaz:
http://arxiv.org/abs/2407.14185
Autor:
Löhr, Thomas, Assante, Michele, Dodds, Michael, Cao, Lili, Kabeshov, Mikhail, Janet, Jon-Paul, Klähn, Marco, Engkvist, Ola
Many computational chemistry and molecular simulation workflows can be expressed as graphs. This abstraction is useful to modularize and potentially reuse existing components, as well as provide parallelization and ease reproducibility. Existing tool
Externí odkaz:
http://arxiv.org/abs/2402.10064
Deep learning-based approaches for generating novel drug molecules with specific properties have gained a lot of interest in the last few years. Recent studies have demonstrated promising performance for string-based generation of novel molecules uti
Externí odkaz:
http://arxiv.org/abs/2303.17615
Autor:
Oldenhof, Martijn, Ács, Gergely, Pejó, Balázs, Schuffenhauer, Ansgar, Holway, Nicholas, Sturm, Noé, Dieckmann, Arne, Fortmeier, Oliver, Boniface, Eric, Mayer, Clément, Gohier, Arnaud, Schmidtke, Peter, Niwayama, Ritsuya, Kopecky, Dieter, Mervin, Lewis, Rathi, Prakash Chandra, Friedrich, Lukas, Formanek, András, Antal, Peter, Rahaman, Jordon, Zalewski, Adam, Heyndrickx, Wouter, Oluoch, Ezron, Stößel, Manuel, Vančo, Michal, Endico, David, Gelus, Fabien, de Boisfossé, Thaïs, Darbier, Adrien, Nicollet, Ashley, Blottière, Matthieu, Telenczuk, Maria, Nguyen, Van Tien, Martinez, Thibaud, Boillet, Camille, Moutet, Kelvin, Picosson, Alexandre, Gasser, Aurélien, Djafar, Inal, Simon, Antoine, Arany, Ádám, Simm, Jaak, Moreau, Yves, Engkvist, Ola, Ceulemans, Hugo, Marini, Camille, Galtier, Mathieu
To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n{\deg}831472), which was comprised of 10 pharmaceutical companies, academic research
Externí odkaz:
http://arxiv.org/abs/2210.08871
Autor:
Svensson, Hampus Gummesson, Bjerrum, Esben Jannik, Tyrchan, Christian, Engkvist, Ola, Chehreghani, Morteza Haghir
Recent developments in artificial intelligence and automation support a new drug design paradigm: autonomous drug design. Under this paradigm, generative models can provide suggestions on thousands of molecules with specific properties, and automated
Externí odkaz:
http://arxiv.org/abs/2207.01393