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MOLDIVS: Molecular Diversity and Similarity


Welcome to MOLDIVS

This manual explains how to use MOLDIVS to perform similarity and diversity calculations on structural databases of chemical compounds. After the Introduction, it is organized by chapters, each describing the actions and utilities of the main menu items.

Overview of MOLDIVS

MOLDIVS (MOLecular DIVersity and Similarity) is a program package for molecular similarity and diversity calculations for Microsoft Windows. MOLDIVS permits to perform a wide range of similarity and diversity calculation tasks on the large sets of compounds.

Program is oriented on specialists in Compounds Selection and Acquisition, High-Throughput Screening, Combinatorial Chemistry, Medicinal Chemistry, Computational Chemistry, Chemical Informatics, Structure-Activity Relationships and Chemical Databases.

With MOLDIVS you will be able to…
  • Calculate similarity indexes for any chemical compound with all compounds in any database.
  • Calculate the complete similarity matrix for any database of chemical compounds.
  • Estimate the whole diversity of any database of chemical compounds.
  • Select diverse subset of compounds from any database of chemical compounds.
  • Selectively import subset of dissimilar compounds from external database.
Program Features
General
  • Microsoft Windows Compatible.
  • Friendly Graphic User Interface.
  • Structure Editor.
  • Database Management System.
  • MDL SD File Import/Export.
Structural Fragments
  • Atom-Centered Concentric Environments with Sphere of Any Size.
  • Plain Structural Fragments and Combined Structural-Physicochemical Fragments.
  • Partial Atomic Charge, Polarizability and H-Bond Donor/Acceptor Factor Parameters.
  • Fragments Visualization.
  • Unlimited Number of Fragments.
  • Fragments Frequency of Occurrence Estimation.
Similarity and Diversity Calculations
  • Three Similarity Measures.
  • Two Measures of Diversity.
  • Fast Database Diversity Estimation.
  • Full Similarity Matrix Calculation.
Compound Selection Algorithms
  • Sum(Min.Dissimilarities) maximization.
  • Min.Dissimilarities maximization.
  • Sum(Dissimilarities) maximization.
  • Stepwise Elimination.
  • Cluster Sampling.
  • Selective Import from External Databases.