Zobrazeno 1 - 10
of 302
pro vyhledávání: '"DE FABRITIIS, Gianni"'
All-atom molecular simulations offer detailed insights into macromolecular phenomena, but their substantial computational cost hinders the exploration of complex biological processes. We introduce Advanced Machine-learning Atomic Representation Omni-
Externí odkaz:
http://arxiv.org/abs/2409.17852
Autor:
De Fabritiis, Gianni
Machine learning potentials offer a revolutionary, unifying framework for molecular simulations across scales, from quantum chemistry to coarse-grained models. Here, I explore their potential to dramatically improve accuracy and scalability in simula
Externí odkaz:
http://arxiv.org/abs/2408.12625
Publikováno v:
Sci Data 11, 1299 (2024)
Recent advancements in protein structure determination are revolutionizing our understanding of proteins. Still, a significant gap remains in the availability of comprehensive datasets that focus on the dynamics of proteins, which are crucial for und
Externí odkaz:
http://arxiv.org/abs/2407.14794
Binding affinity optimization is crucial in early-stage drug discovery. While numerous machine learning methods exist for predicting ligand potency, their comparative efficacy remains unclear. This study evaluates the performance of classical tree-ba
Externí odkaz:
http://arxiv.org/abs/2407.19073
Small molecule protonation is an important part of the preparation of small molecules for many types of computational chemistry protocols. For this, a correct estimation of the pKa values of the protonation sites of molecules is required. In this wor
Externí odkaz:
http://arxiv.org/abs/2407.11103
We present BricksRL, a platform designed to democratize access to robotics for reinforcement learning research and education. BricksRL facilitates the creation, design, and training of custom LEGO robots in the real world by interfacing them with the
Externí odkaz:
http://arxiv.org/abs/2406.17490
Autor:
Bou, Albert, Thomas, Morgan, Dittert, Sebastian, Ramírez, Carles Navarro, Majewski, Maciej, Wang, Ye, Patel, Shivam, Tresadern, Gary, Ahmad, Mazen, Moens, Vincent, Sherman, Woody, Sciabola, Simone, De Fabritiis, Gianni
In recent years, reinforcement learning (RL) has emerged as a valuable tool in drug design, offering the potential to propose and optimize molecules with desired properties. However, striking a balance between capabilities, flexibility, reliability,
Externí odkaz:
http://arxiv.org/abs/2405.04657
Autor:
Simeon, Guillem, Mirarchi, Antonio, Pelaez, Raul P., Galvelis, Raimondas, De Fabritiis, Gianni
In this letter, we present an extension to TensorNet, a state-of-the-art equivariant Cartesian tensor neural network potential, allowing it to handle charged molecules and spin states without architectural changes or increased costs. By incorporating
Externí odkaz:
http://arxiv.org/abs/2403.15073
Autor:
Pelaez, Raul P., Simeon, Guillem, Galvelis, Raimondas, Mirarchi, Antonio, Eastman, Peter, Doerr, Stefan, Thölke, Philipp, Markland, Thomas E., De Fabritiis, Gianni
Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents substantial advancements in the TorchMD-Net software, a pivotal step forwa
Externí odkaz:
http://arxiv.org/abs/2402.17660
Autor:
Zariquiey, Francesc Sabanes, Galvelis, Raimondas, Gallicchio, Emilio, Chodera, John D., Markland, Thomas E., de Fabritiis, Gianni
This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM). We compu
Externí odkaz:
http://arxiv.org/abs/2401.16062