FRED Pose Prediction and Virtual Screening Accuracy

نویسنده

  • Mark McGann
چکیده

Results of a previous docking study are reanalyzed and extended to include results from the docking program FRED and a detailed statistical analysis of both structure reproduction and virtual screening results. FRED is run both in a traditional docking mode and in a hybrid mode that makes use of the structure of a bound ligand in addition to the protein structure to screen molecules. This analysis shows that most docking programs are effective overall but highly inconsistent, tending to do well on one system and poorly on the next. Comparing methods, the difference in mean performance on DUD is found to be statistically significant (95% confidence) 61% of the time when using a global enrichment metric (AUC). Early enrichment metrics are found to have relatively poor statistical power, with 0.5% early enrichment only able to distinguish methods to 95% confidence 14% of the time.

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

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Docking and Scoring with Target-Specific Pose Classifier Succeeds in Native-Like Pose Identification But Not Binding Affinity Prediction in the CSAR 2014 Benchmark Exercise

The CSAR 2014 exercise provided an important benchmark for testing current approaches for pose identification and ligand ranking using three X-ray characterized proteins: Factor Xa (FXa), Spleen Tyrosine Kinase (SYK), and tRNA Methyltransferase (TRMD). In Phase 1 of the exercise, we employed Glide and MedusaDock docking software, both individually and in combination, with the special target-spe...

متن کامل

How to do an evaluation: pitfalls and traps

The recent literature is replete with papers evaluating computational tools (often those operating on 3D structures) for their performance in a certain set of tasks. Most commonly these papers compare a number of docking tools for their performance in cognate re-docking (pose prediction) and/or virtual screening. Related papers have been published on ligand-based tools: pose prediction by confo...

متن کامل

Discrete molecular dynamics distinguishes nativelike binding poses from decoys in difficult targets.

Virtual screening is one of the major tools used in computer-aided drug discovery. In structure-based virtual screening, the scoring function is critical to identifying the correct docking pose and accurately predicting the binding affinities of compounds. However, the performance of existing scoring functions has been shown to be uneven for different targets, and some important drug targets ha...

متن کامل

بهبود بازشناسی چهره با یک تصویر از هر فرد به روش تولید تصاویر مجازی توسط شبکه‌های عصبی

This paper deals with the problem of face recognition from a single image per person by producing virtual images using neural networks. To this aim, the person and variation information are separated and the associated manifolds are estimated using a nonlinear neural information processing model. For increasing the number of training samples in neural classifier, virtual images are produced for...

متن کامل

A Machine Learning Approach to Enhance Scoring Performance in Docking-Based Virtual Screening Experiments: COX-1 as a Case Study

Molecular docking can be reasonably successful at reproducing X-ray poses of a ligand in the binding site of a protein. However, scoring functions are typically unsuccessful at correctly ranking ligands according to their binding affinity. Using cyclooxygenase-1 (COX-1), a particularly challenging workhorse in virtual screening (VS) we show how the use of support vector machines (SVMs), trained...

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

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

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Journal of chemical information and modeling

دوره 51 3  شماره 

صفحات  -

تاریخ انتشار 2011