Machine Learning Estimates of Natural Product Conformational Energies
نویسندگان
چکیده
منابع مشابه
Machine Learning Estimates of Natural Product Conformational Energies
Machine learning has been used for estimation of potential energy surfaces to speed up molecular dynamics simulations of small systems. We demonstrate that this approach is feasible for significantly larger, structurally complex molecules, taking the natural product Archazolid A, a potent inhibitor of vacuolar-type ATPase, from the myxobacterium Archangium gephyra as an example. Our model estim...
متن کاملMachine Learning of Natural Language
In this article we provide an overview of recent research on the application of symbolic Machine Learning techniques to language data (Machine Learning of Natural Language, MLNL). Both in Quantitative Linguistics (QL) and in MLNL, the main goal is to describe the language as it is observed with rules, language models, or other descriptions. But whereas the motivation in QL is purely scientific ...
متن کاملWeighted quality estimates in machine learning
MOTIVATION Machine learning methods such as neural networks, support vector machines, and other classification and regression methods rely on iterative optimization of the model quality in the space of the parameters of the method. Model quality measures (accuracies, correlations, etc.) are frequently overly optimistic because the training sets are dominated by particular families and subfamili...
متن کاملModeling of molecular atomization energies using machine learning
Atomization energies are an important measure of chemical stability. Machine learning is used to model atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only [1]. Our scheme maps the problem of solving the molecular time-independent Schrödinger equation onto a non-linear statistical regression problem. Kernel ridge regression [2] models ar...
متن کاملApplying Machine Learning to Product Categorization
We present a method for classifying products into a set of known categories by using supervised learning. That is, given a product with accompanying informational details such as name and descriptions, we group the product into a particular category with similar products, e.g., ‘Electronics’ or ‘Automotive’. To do this, we analyze product catalog information from different distributors on Amazo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PLoS Computational Biology
سال: 2014
ISSN: 1553-7358
DOI: 10.1371/journal.pcbi.1003400