Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials [0.03%]
基于机器学习的原子间势能的显式溶剂中的激发态非绝热动力学
Maximilian X Tiefenbacher,Brigitta Bachmair,Cheng Giuseppe Chen et al.
Maximilian X Tiefenbacher et al.
Excited-state nonadiabatic simulations with quantum mechanics/molecular mechanics (QM/MM) are essential to understand photoinduced processes in explicit environments. However, the high computational cost of the underlying quantum chemical c...
Paddy: an evolutionary optimization algorithm for chemical systems and spaces [0.03%]
一种用于化学体系和空间的进化优化算法
Armen G Beck,Sanjay Iyer,Jonathan Fine et al.
Armen G Beck et al.
Optimization of chemical systems and processes have been enhanced and enabled by the development of new algorithms and analytical approaches. While several methods systematically investigate how underlying variables correlate with a given o...
A workflow to create a high-quality protein-ligand binding dataset for training, validation, and prediction tasks [0.03%]
用于训练、验证和预测任务的高质量蛋白质-配体结合数据集的工作流程
Yingze Wang,Kunyang Sun,Jie Li et al.
Yingze Wang et al.
Development of scoring functions (SFs) used to predict protein-ligand binding energies requires high-quality 3D structures and binding assay data for training and testing their parameters. In this work, we show that one of the widely-used d...
Kenneth López Pérez,Vicky Jung,Lexin Chen et al.
Kenneth López Pérez et al.
The widespread use of Machine Learning (ML) techniques in chemical applications has come with the pressing need to analyze extremely large molecular libraries. In particular, clustering remains one of the most common tools to dissect the ch...
ULaMDyn: enhancing excited-state dynamics analysis through streamlined unsupervised learning [0.03%]
基于流线型无监督学习的激发态动力学分析增强方法（ULaMDyn）
Max Pinheiro Jr,Matheus de Oliveira Bispo,Rafael S Mattos et al.
Max Pinheiro Jr et al.
The analysis of nonadiabatic molecular dynamics (NAMD) data presents significant challenges due to its high dimensionality and complexity. To address these issues, we introduce ULaMDyn, a Python-based, open-source package designed to automa...
Biophysics-guided uncertainty-aware deep learning uncovers high-affinity plastic-binding peptides [0.03%]
生物物理引导的不确定性感知深度学习发现高亲和力塑料结合肽
Abdulelah S Alshehri,Michael T Bergman,Fengqi You et al.
Abdulelah S Alshehri et al.
Plastic pollution, particularly microplastics (MPs), poses a significant global threat to ecosystems and human health, necessitating innovative remediation strategies. Biocompatible and biodegradable plastic-binding peptides (PBPs) offer a ...
Predicting hydrogen atom transfer energy barriers using Gaussian process regression [0.03%]
用高斯过程回归预测氢原子转移的能量屏障
Evgeni Ulanov,Ghulam A Qadir,Kai Riedmiller et al.
Evgeni Ulanov et al.
Predicting reaction barriers for arbitrary configurations based on only a limited set of density functional theory (DFT) calculations would render the design of catalysts or the simulation of reactions within complex materials highly effici...
Qianxiang Ai,Fanwang Meng,Runzhong Wang et al.
Qianxiang Ai et al.
Automated chemistry platforms hold the potential to enable large-scale organic synthesis campaigns, such as producing a library of compounds for biological evaluation. The efficiency of such platforms will depend on the schedule according t...
Active learning driven prioritisation of compounds from on-demand libraries targeting the SARS-CoV-2 main protease [0.03%]
基于主动学习的针对SARS-CoV-2主要蛋白酶的需求化合物库的优先排序
Ben Cree,Mateusz K Bieniek,Siddique Amin et al.
Ben Cree et al.
FEgrow is an open-source software package for building congeneric series of compounds in protein binding pockets. For a given ligand core and receptor structure, it employs hybrid machine learning/molecular mechanics potential energy functi...
A hitchhiker's guide to deep chemical language processing for bioactivity prediction [0.03%]
深度化学语言处理在生物活性预测中的指南
Rıza Özçelik,Francesca Grisoni
Rıza Özçelik
Deep learning has significantly accelerated drug discovery, with 'chemical language' processing (CLP) emerging as a prominent approach. CLP approaches learn from molecular string representations (e.g., Simplified Molecular Input Line Entry ...