MOLDIVS
 Molecular diversity and similarity estimation
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 95/98/NT. 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 95/98/NT 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.
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