Refinement of chemical libraries for selecting subsets for experimental screening. The subsets are chosen based on predefined requirements that are evaluated in silico (solubility, logP, toxicities, etc).
Development of predictive models based on customer's in-house experimental data and/or public sources. This service also includes literature and data base search, data management, structure optimization and expert opinion.
In silico prediction of biological activities, environmental fate, toxicological endpoints of compounds (and mixtures).
Evaluation of ADME/Tox properties and medicinal effects, based on in silico models and expert judgment.
Volume calculations - structure optimization using Molecular Mechanics, QM (semi-empirical, DFT, ab initio) methods. Dynamic modeling using Molecular Dynamics, Monte Carlo methods.
Predictive QSAR models for physicochemical properties, ADME/Tox, toxicological, environmental and medicinal properties/activities.
Molcode’s proprietary reverse-QSAR technology enables to design structures for industrial chemicals that carry predetermined properties. Successful examples include applications ranging from adsorbents to repellents.
Molcode has several tools and capabilities to participate in drug design projects as a partner in fields of computational design and engineering.
Scaffold based hit and lead optimizations, early screening of possible side effects and fine tuning of the small molecule candidates.
Molcode has developed 1000+ original molecular descriptor's which are calculated solely from the molecular structure. Our statistical tools enable to select optimum numbers of molecular descriptor's which have the greatest impact to the target property/activity. Custom descriptors can be developed to focus on target property and/or structural features of a particular data set.
In silico estimation of physical properties of pure compounds and mixtures. Filling data-gaps for process analysis, model building, etc using quantum chemical, Monte Carlo, molecular dynamics, etc methods.