APPLICATION OF SUPPORT VECTOR MACHINES IN VIRTUAL SCREENING

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Virtual screening with support vector machines and structure kernels

Support vector machines and kernel methods have recently gained considerable attention in chemoinformatics. They offer generally good performance for problems of supervised classification or regression, and provide a flexible and computationally efficient framework to include relevant information and prior knowledge about the data and problems to be handled. In particular, with kernel methods m...

متن کامل

The Pharmacophore Kernel for Virtual Screening with Support Vector Machines

We introduce a family of positive definite kernels specifically optimized for the manipulation of 3D structures of molecules with kernel methods. The kernels are based on the comparison of the three-point pharmacophores present in the 3D structures of molecules, a set of molecular features known to be particularly relevant for virtual screening applications. We present a computationally demandi...

متن کامل

3D Ligand-Based Virtual Screening with Support Vector Machines

Computational models play an important role in early-stage drug discovery, in particular for lead identification and optimization. Starting from a list of molecules with experimentally determined binding affinity to a particular therapeutic target, as typically obtained by high-throughput screening (HTS), the goal of lead optimization is to find additional molecules with good binding affinity. ...

متن کامل

STAGE-DISCHARGE MODELING USING SUPPORT VECTOR MACHINES

Establishment of rating curves are often required by the hydrologists for flow estimates in the streams, rivers etc. Measurement of discharge in a river is a time-consuming, expensive, and difficult process and the conventional approach of regression analysis of stage-discharge relation does not provide encouraging results especially during the floods. P

متن کامل

Safe screening for support vector machines

The L2-regularized hinge loss kernel SVM could be the most important and most studied machine learning algorithm. Unfortunately, its computational training time complexity is generally unsuitable for big data. Empirical runtimes can however often be reduced using shrinking heuristics on the training sample set, which exploit the fact that non-support vectors do not affect the decision boundary....

متن کامل

ذخیره 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: International Journal for Computational Biology

سال: 2012

ISSN: 2278-8115

DOI: 10.34040/ijcb.1.1.2012.20