Core A Software
|TargetHunter: Ligand-based target prediction tools with BioassayGeoMap function integrated http://www.cbligand.org/TargetHunter|
|HTDocking: Structure-based target prediction tools for drug repurpose research. http://www.cbligand.org/HTDocking/|
|BBB Predictor: Machine learning algorithms- AdaBoost and SVM-based tool is designed for predicting the permeability of blood-brain barrier (BBB) for compounds. http://www.cbligand.org/BBB/|
|GPU-Accelerated Compound Library Comparison: Modern graphics process units (GPU) –based parallel computing, millions of compound comparison can be accomplished in a few seconds. http://www.cbligand.org/gpu/|
|LiCABEDS: LiCABEDS (Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps) is designed to help pharmacologists and medicinal chemists to easily use machine learning techniques for virtual screening and quantitative structure-activity relationship (QSAR) study. http://www.cbligand.org/LiCABEDS/|
|FANN-QSAR: Fingerprint-based artificial neural network QSAR (FANN-QSAR) method is to effectively predict biological activities of structurally diverse chemical ligands. FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research. http://www.cbligand.org/cbid/ANN_Activity_predictor.php|
|DAKB: Chemogenomics Database for Drug abuse Research is designed for facilitating data-sharing and information exchange among scientific research communities for drug abuse, including genes, proteins, small molecules and signal pathways, with online structure search functions and data analysis tools implemented. http://www.cbligand.org/CDAR
More technologies for core A can be found in our CCGS center. http://www.cbligand.org/CCGS/technology.php
Core B Software
|DynOmics ENM server computes biomolecular systems dynamics by integrating two widely used elastic network models (ENMs) – the Gaussian Network Model (GNM) and the Anisotropic Network Model (ANM). Unique features include the consideration of environment, the prediction of potential functional sites and reconstruction of all-atom conformers from deformed coarse-grained structures. http://enm.pitt.edu/|
|iGNM 2.0 is a Gaussian Network Model (GNM) Database where users may access the results for the equilibrium dynamics of any given biomolecular structure, or corresponding biological assembly, structurally characterized to date. http://gnmdb.csb.pitt.edu|
|ProDy is an Application Programming Interface, for sequence- and structure-based studies of functional mechanisms and collective dynamics of biomolecular systems. It is a free and open-source Python package. It has fast, flexible and powerful file parsers and customizable atom-selections for structural analyses and for bridging sequence evolution and structural dynamics http://prody.csb.pitt.edu/|
|BalestraWeb Server addresses the need for efficient identification of potential interactions between drugs and targets, and identification of repurposable drugs, or side effects of known drugs, using a latent factor model of drug-target interactions. http://balestra.csb.pitt.edu/|
|ANM is a Normal Mode Analysis (NMA) Server for analysis of collective motions of biomolecular systems using the anisotropic network model (ANM). The ANM is an elastic network model (ENM) where the system is represented by a network of nodes connected by elastic springs. It lends itself to fast and accurate evaluation of cooperative motions for large complexes and assemblies. http://anm.csb.pitt.edu/cgi-bin/anm2/anm2.cgi|
Core C Software
|GenAMap： a visualization strategies for structured association mapping. GWAS is in a box: statistical and visual analytics of structured associations via GenAMapand, a visual Machine Learning for Next Generation GWAS|
|SPHINX: An Efficient Nonlinear Regression Approach for Genome-wide Detection of Marginal and Interacting Genetic Variations.|
TVNViewer: An interactive visualization tool for exploring networks that change over time or space.
|Precision Lasso: Accounting for Correlations and Linear Dependencies in High-Dimensional Genomic Data|
|LRVA: Discovering Weaker Genetic Associations with Validated Association, with Studies of Alzheimer’s Disease and Drug Abuse Disorder
|BAYCIS: A generalized, hierarchical HMM for transcription factor binding site discovery in Metazoan genomes.
|Structured Input-output Lasso: Detecting SNPs associated with traits considering both genome (input) and transcriptome (output) structures.