Efficient iterative virtual screening with Apache Spark and conformal prediction
Abstract Background Docking and scoring large libraries of ligands against target proteins forms the basis of structure-based virtual screening. The problem is trivially parallelizable, and calculations are generally carried out on computer clusters or on large workstations in a brute force manner,...
Main Authors: | Laeeq Ahmed, Valentin Georgiev, Marco Capuccini, Salman Toor, Wesley Schaal, Erwin Laure, Ola Spjuth |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2018-03-01
|
Series: | Journal of Cheminformatics |
Subjects: | |
Online Access: | http://link.springer.com/article/10.1186/s13321-018-0265-z |
Similar Items
-
Predicting target profiles with confidence as a service using docking scores
by: Laeeq Ahmed, et al.
Published: (2020-10-01) -
Apache Spark ile Makine Öğrenmesi Destekli Diyabet Rahatsızlığı Tahmini
by: Emre Yıldırım, et al.
Published: (2022-07-01) -
Framing Apache Spark in life sciences
by: Andrea Manconi, et al.
Published: (2023-02-01) -
Performance Evaluation of Query Plan Recommendation with Apache Hadoop and Apache Spark
by: Elham Azhir, et al.
Published: (2022-09-01) -
Using Apache Spark on genome assembly for scalable overlap-graph reduction
by: Alexander J. Paul, et al.
Published: (2019-10-01)