ML concepts such as statistical estimator theory, end-to-end learning, representation learning, and active learning are highly interesting for the MD researcher and will help to develop new solutions to hard MD problems. With the aim of better connecting the MD and ML research areas and spawning...
Reviewhttps://doi.org/10.1038/s41586-018-0337-2Machine learning for molecular and materials science Keith T. Butler 1 , Daniel w. Davies 2 , Hugh Cartwright 3 , Olexandr isayev 4 * & Aron walsh 5,6 *Here we summarize recent progress in machine learning for the chemical sciences. We...
Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future i
Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future i
分子对接也可用于检测潜在的药物副作用或分子毒性。分子对接利用配体和靶标的三维结构,预测配体相对于靶标在相互结合形成稳定复合物时的最佳方向。在药物发现中,配体是一种活性物质,靶标是生物大分子(如蛋白质或DNA)。然而,这种对接涵盖了更广泛的配对可能性:蛋白质- DNA、蛋白质- RNA、蛋白质-糖、蛋白质-多肽和...
mdlearn is a Python library for analyzing molecular dynamics with machine learning. It contains PyTorch implementations of several deep learning methods such as autoencoders, as well as preprocessing functions which include the kabsch alignment algorithm and higher-order statistical methods like quasi-an...
Nanoparticle corona phase (CP) design offers a unique approach toward molecular recognition (MR) for sensing applications. Single-walled carbon nanotube (SWCNT) CPs can additionally transduce MR through its band-gap photoluminescence (PL). While DNA olig
Taken together, we here describe a new, adaptive machine learning pre-processing approach and provide novel insights into the behavior and robustness of active machine learning for molecular sciences. Active machine learning can be used to sample training data in an autonomous manner to improve ...
A recent paper authored by Lincan Fang, Esko Makkonen, Milica Todorovic, Patrick Rinke, and Xi Chen proposes a molecular conformer search procedure that combines anactive learningBayesian optimization (BO) algorithm with quantum chemistry methods to address this challenge. The BO active learning smartl...
Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing machine learning models that can achieve this on the ...