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Scaffold Hunter
Allows intuitive hierarchical structuring, visualization and analysis of complex structure and bioactivity data. Scaffold Hunter reads data, for example, from biochemical screens, extracts chemically meaningful compound scaffolds (that is, all carbo- and heterocyclic rings, their aliphatic linker bonds and atoms attached via a double bond) and iteratively removes one ring at a time from the larger child scaffolds to generate smaller ‘parent’ scaffolds according to a set of chemistry- and medicinal chemistry–derived rules. Scaffold Hunter has its origin in drug discovery, which is still one of the main application areas, and is evolved into a reusable open source platform for a wider range of applications.
Decision Forest
Outperforms decision tree in both training and validation. Decision forest is an ensemble method developed by combining the predictions from multiple independent decision tree models to reach a better prediction. This method yields much high prediction accuracy in the high confidence regions compared to decision tree. Decision forest generally gives higher positive predictivity than other method, and even higher positive predictivity within definable high confidence regions.
NetDecoder
A network biology-based computational platform designed to integrate transcriptomes, interactomes and gene ontologies to identify phenotype-specific subnetworks. NetDecoder is based on network flow algorithm and formulated as a minimum-cost flow optimization problem to identify and prioritize paths and key regulators within disease specific subnetworks. NetDecoder is designed to capture molecular switches and infer disease-specific networks to better understand pathways and identify key regulators that contribute to a disease phenotype.
ARQiv-HTS
Prepares and completes whole-organism screening at high-througput rates. ARQiv-HTS includes functions that fall into two categories - those applied to 'Pre-screening Assay Optimization' and 'Compound Analysis'. The functions allow the user to calculate background signal, determine sample size, run quality control tests, perform virtual experiments to simulate compound efficacy - and finally, to perform compound analysis during iterative drug screen cycles. ARQiv-HTS platform is adaptable to almost any reporter-based assay designed to evaluate the effects of chemical compounds in living small-animal models. ARQiv-HTS thus enables large-scale whole-organism drug discovery for a variety of model species and from numerous disease-oriented perspectives.
CoPIA / Combinatorial Perturbation Interaction Analysis
Constructs, optimizes and applies computational models of cellular processes. CoPIA is a program that combines model construction in terms of nonlinear differential equations, combinatorial intervention, molecular observation at multiple points, optimization of model parameters with simplicity constraints and experimental validation. The software can be used to build reasonably accurate quantitative predictors of pathway responses to combinatorial drug perturbation in MCF7 cells.
Mona
Allows an intuitive interactive organization and exploration of chemical small molecule datasets on current desktop computer hardware. Mona is a generic, simple, and interactive compound browser and manager. This package contains two separate algorithms to cluster molecule sets. First, clustering properties employ bins to create clusters of molecules with similar property values. Second, clustering by similarity calculates fingerprints for all molecules, employing the k-medoid algorithm, which requires quadratic time.
molBLOCKS
Allows users to break down small molecules into chemically meaningful fragments. molBLOCKS permits analyzing the resulting fragment distribution. It consists of two command-line programs: (1) “fragment” that reads user- defined rules to specify the bonds to break or uses default sets of rules; and (2) “analyze” that collects statistics on the frequency, clusters fragments using a user-defined similarity threshold based on a fingerprint representation of the fragment and selects a representative fragment for each cluster.
Intercalate
Creates the 3D structure of DNA from the DNA sequence along with an intercalation site, and docks a ligand at the intercalation site. Intercalate is a state-of-the-art, robust and dedicated drug-DNA intercalation methodology. This method also predicts the best binding mode of ligand efficiently through binding free energy estimations in an automated mode. Intercalate methodology holds the potential for the identification of new ligand molecules intercalating to DNA non-covalently. A webserver is also created based on the proposed methodology and is made freely accessible. The webserver contains both the datasets, all the structures used in developing the methodology and a guide to its usage.
Collector
Allows extraction of data from the ‘Open Pharmacological Space’ (OPS) developed by the Open PHACTS project. Collector is an application that extracts series of compounds from OPS, together with the result of pharmacological/toxicological experiments. The software applies customizable curation filters, produces series of compounds in a format well suited for the development of quantitative structure-activity relationships (QSAR models). It was used in the eTOX project for the development of QSAR models against targets considered of toxicological interest (anti-targets).
iATC-mHyb
Predicts the Anatomical Therapeutic Chemical (ATC) classes. It has been established by hybridizing of the iATC-mISF method with the powerful iATC-mDO sub-predictor. iATC-mHyb outperforms the best existing ATC predictor in all the five metrics used to examine the prediction quality of a predictor for multi-label systems, particularly in the “absolute true” rate and the “absolute false” rate, the two most difficult to-improve indexes. This multi-label predictor can achieve lower than 3% of absolute false rate.
SMSD / Small Molecule Subgraph Detector
Allows users to find the maximum common subgraph (MCS) in small molecules. SMSD uses a combination of various algorithms to search the MCS and filters the results in a manner that is chemically relevant. This tool calculates the MCS between two molecules by combining the power of the VF+ Lib, the MCS+, and the Chemistry Development Kit (CDK) based MCS algorithm. It checks if two molecules are identical or dissimilar based on the atom count and bond count before performing the MCS search.
AZOrange
Supports batch generation of quantitative structure-activity relationship (QSAR) models. AZOrange is based on a simple generalized consensus model. It combines the predictions from the tool’s learners by averaging or by using the majority vote. The tool uses customized high performance state-of-the-art machine learning algorithms. It offers a way to automatically build QSAR pipelines. AZOrange is able to transform data formats, scale descriptor values where appropriate, accommodate missing values and select stopping criteria.
WENDI / Web Engine for Non-obvious Drug Information
An integrative data mining tool for drug discovery using aggregate web services. WENDI aims to build a full picture of potential biological activities of a chemical compound through the aggregation of data from web services that represent diverse multiple sources (including predictive models, databases and journal articles). WENDI allows the identification of corroborating or conflicting information: for instance, a compound might be predicted active in a breast cancer cell line, and similar compounds might show active in a PubChem BioAssay related to breast cancer, or be co-located in a paper abstract with a breast cancer related gene.
ClassyFire
Performs automated rule-based taxonomic classification of chemical compounds. ClassyFire is a web-accessible computer program that allows automated rule-based structural classification of all known chemical entities. The software is built around a chemical taxonomy along with a fully annotated chemical ontology (ChemOnt) and a Chemical Classification Dictionary. The ClassyFire API allows users to programmatically access the web server for submitting queries, and retrieving classification results, as well as entity-related properties.
Badapple / Bioassay-data associative promiscuity pattern learning engine
An easy-to-use, readily interpretable algorithm and tool that can assist scientists in navigating a complex scientific and informational landscape. In particular, Badapple is designed for rapid detection of promiscuity patterns in HTS data, using public bioassay evidence. However, Badapple is designed to be trained with additional data, and to detect novel patterns, based on an entirely different chemical library. Compound promiscuity is generally undesirable but must be understood in light of polypharmacology and systems chemical biology. Badapple scores indicate either patterns of true or artefactual promiscuity, either of which can help guide an experimental research project away from “false trails”.
LEMONS / Library for the Enumeration of MOdular Natural Structures
Enumerates hypothetical modular natural product structures. LEMONS is an extensible method that allows modification of their monomer composition or tailoring reactions, and comparing of the original and modified structures using 2D molecular fingerprints. It enables to define a true match between the original and modified scaffolds originating from the same in silico assembly line and thus derived the proportion of correct matches between original and modified structures for each fingerprint. It can be useful in chemical space exploration and microbial genome mining.
VIDEAN / Visual and Interactive DEscriptor ANalysis
Compares alternative descriptor subsets. VIDEAN is an interactive visual analytics application that combines statistical methods with interactive visualizations for choosing a set of descriptors for predicting a target property. Coordinated visual representations are presented for capturing different relationships and interactions among descriptors, target properties and candidate subsets of descriptors. Some domain expertise can also be added to the feature selection process by means of an interactive visual exploration of data.
ConfGen
Generates bioactive conformers of drug-like molecules. ConfGen is based on the infrastructure from the general molecular modeling program MacroModel. It allows access to multiple all-atom force fields, redundant conformer elimination, and multiple processor computing. The tool offers features to limit the number of ring system conformations sampled, including an upper limit, a maximum number of the lowest energy ring conformations per ring system to use and a maximum overall number of ring conformations.
Mold(2)
Enables the rapid calculation of a large and diverse set of descriptors encoding 2D chemical structure information. Mold(2) easily and quickly calculates molecular descriptors with no missing values, a common problem with most existing commercial systems. The descriptors used by this tool were compared with descriptors from commercial software packages using information entropy analysis, analysis of correlations between descriptors, and Decision Forest classification on several reported data sets.
LiCABEDS / Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps
Assists in classifing 5-HT1A ligand functionality. LiCABEDS is a generic ligand classification algorithm for the prediction of categorical molecular properties. It was developed for the prediction of categorical ligand properties. It minimizes training error by iteratively adding more “learners” into the classifier ensemble. The implementation includes automated model training, cross-validation, and predicting.
PaDEL-descriptor
Calculates molecular descriptors and fingerprints. PaDEL-descriptor was developed using the Java language and consists of a library and an interface component. It currently calculates 797 descriptors (663 1D, 2D descriptors, and 134 3D descriptors) and 10 types of fingerprints. These descriptors and fingerprints are calculated mainly using The Chemistry Development Kit. Some additional descriptors and fingerprints were added, which include atom type electrotopological state descriptors, McGowan volume, molecular linear free energy relation descriptors, ring counts, count of chemical substructures identified by Laggner, and binary fingerprints and count of chemical substructures identified by Klekota and Roth. PaDEL-descriptor is free and open source, which has both graphical user interface (GUI) and command line interfaces, able to work on all major platforms (Windows, Linux, MacOS), supports more than 90 different molecular file formats, and is multithreaded.
DemQSAR
A publicly available application for quantitative structure-activity relationship (QSAR) analysis. DemQSAR integrates the open source CDK library to compute various molecular descriptors and fingerprints. QSAR models can thus be built without any additional software. Only two dimensional structures of the molecules have to be provided. CDK features can be easily complemented by external feature sets computed by other software. DemQSAR incorporates two state of the art feature selection strategies: embedded Lasso and recursive feature elimination. A standalone DemQSAR Java application for model building of any user defined property as well as a web interface for the prediction of human volume of distribution (VDss) and human clearance (CL) is available on the webpage of DemPRED.
APPLE / Applying QSAR for Predicting Pan-Inhibitor of Bcl-2 Family Proteins
Predicts a pan- or specific inhibitor for Bcl-2 and Bcl-xL targets. APPLE is a web-server that classifies the input according to its core and then predicts IC50 from the model generated for the family. The software can also identify the specificity of compounds toward antiapoptotic proteins Bcl-2 and Bcl-xL. The software finally provides the user with the activity and specificity of a given compound. It can be useful for identifying novel specific inhibitor of Bcl-2 family pro-survival proteins.
MOE / Molecular Operating Environment
An integrated drug discovery software. MOE is able to track design ideas and ligand modifications with property models, produce correlation plots to visualize Structure, Property, Activity Relationships and visualize hydrophobic and charged protein surface to study aggregation prone regions. It can also automatically align and superpose antibody structures using the MOE Project protocol, generate and search advanced antibody queries with the Project Search application and build full length Ig structures including bispecifics with the Antibody Homology Modeler.
TOPS / Tool for Combination Perturbation Screen Analysis
Assists in statistical analyzing and visualizing combinatorial gene-gene and gene-drug interaction screens. TOPS permits users to plot, filter, import, and analyze data from double perturbation screens. It incorporates statistical models designed for the analysis of pairwise interactions of larger gene/drug sets. This tool can analyze all types of data, as long as the data can be reduced to a “perturbation A”, “perturbation B”, “score” format.
DrugQuest
A text mining tool to find new associations between drugs. DrugQuest clusters DrugBank records based on their textual information in a multidimensional vector space. We mainly apply partitional clustering algorithms in order to group together DrugBank records based on their textual information. Toxicity, targeted pathways, targeted proteins, diseases and/or other interactors are few examples of such textual information. Uniquely assigning DrugBank records into clusters, based on tagged terms such as pathways diseases, molecules, biological processes, can make DrugQuest a promising tool for new concept discovery and detection of new drug associations.
ActionPotential
Allows users to perform electrophysiology simulations. ActionPotential is an open source portal, also available as a standalone software, which intends to evaluate the performance of different models and to define suitable contexts of use. It has two main functions: (i) gathers data from a cardiac ion channel screening panel and define expectations of the likely total effect, in multiple situations (ii) determines QT liability of compounds and possibly design new experiments.
fraction-who-benefit
Uses to learn about the fraction who benefit from a new treatment using randomized trial data. fraction-who-benefit includes (i) proving the plug-in estimator of the bounds can be inconsistent if support restrictions are made on the joint distribution of the potential outcomes; (ii) developing the first consistent estimator for this case; and (iii) applying this estimator to a randomized trial of a medical treatment to determine whether the estimates can be informative. This estimator is computed using linear programming, allowing fast implementation.
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