Bioactive Conformational Ensemble Server and Database. A Public Framework to Speed Up In Silico Drug Discovery.
Sanja ZivanovicGenís BayarriFrancesco ColizziDavid MorenoJosep Lluís GelpíRobert SolivaAdam HospitalModesto OrozcoPublished in: Journal of chemical theory and computation (2020)
Modern high-throughput structure-based drug discovery algorithms consider ligand flexibility, but typically with low accuracy, which results in a loss of performance in the derived models. Here we present the bioactive conformational ensemble (BCE) server and its associated database. The server creates conformational ensembles of drug-like ligands and stores them in the BCE database, where a variety of analyses are offered to the user. The workflow implemented in the BCE server combines enhanced sampling molecular dynamics with self-consistent reaction field quantum mechanics (SCRF/QM) calculations. The server automatizes all of the steps to transform one-dimensional (1D) or 2D representation of drugs into 3D molecules, which are then titrated, parametrized, hydrated, and optimized before being subjected to Hamiltonian replica-exchange (HREX) molecular dynamics simulations. Ensembles are collected and subjected to a clustering procedure to derive representative conformers, which are then analyzed at the SCRF/QM level of theory. All structural data are organized in a noSQL database accessible through a graphical interface and in a programmatic manner through a REST API. The server allows the user to define a private workspace and offers a deposition protocol as well as input files for "in house" calculations in those cases where confidentiality is a must. The database and the associated server are available at https://mmb.irbbarcelona.org/BCE.
Keyphrases
- molecular dynamics
- molecular dynamics simulations
- drug discovery
- density functional theory
- adverse drug
- protein protein
- molecular docking
- high throughput
- electronic health record
- healthcare
- machine learning
- small molecule
- randomized controlled trial
- single cell
- big data
- health insurance
- rna seq
- mental health
- convolutional neural network
- drug induced
- artificial intelligence
- cross sectional