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Join your peers around the world and start performing AI-driven compound optimization with ADMET Predictor.
What is the NEW! AIDD™ Module?
The AIDD Module integrates ADMET Predictor’s top-ranked ADMET property prediction models with multi-objective compound optimization capabilities:
- Generative chemistry engine
- Advanced and customizable pruning rules
- Multi-objective compound selection based on a Pareto front
- Iterative cycles of optimization based on design criteria
ADMET Predictor AIDD will take one or more starting structures and optimize them against a set of target properties. Activity models can be constructed in ADMET Modeler™ and used as part of the optimization to drive activity against one or multiple targets (e.g., selectivity). All numeric ADMET property prediction models as well as custom models can be used as part of the target profile. This includes mechanistic models from the HTPK Simulation module (%Fa and %Fb).
AIDD takes advantage of the recent architectural enhancement to ADMET Predictor, providing multi-threading capabilities for all currently available ADMET property prediction models. Other custom global, local or delta models can also operate in multi-threaded mode. Taking advantage of these capabilities, AIDD can generate and evaluate up to 5 million molecules in the course of an overnight run (8-core laptop computer) exploring a large portion of chemical space around the compound(s) or scaffold(s) of interest.
AIDD empowers chemists to control which part(s) of the molecule may be altered as part of the optimization and which should be maintained. You can also specify positions where substitutions can be applied, and control the chemistry based on synthetic feasibility constraints or a-priori knowledge about the target.
The analysis tools in ADMET Predictor have been enhanced to provide you with the capabilities to sift through and analyze results from AIDD runs. You can analyze intermediate results of each generative cycle (even while the AIDD run is still in progress) as well as examine final results to see which compounds have the best combination of properties.
 Robert D. Clark, Ph.D. Sr. Research Fellow Simulations Plus, Inc. |
 Marvin Waldman, Ph.D. Sr. Research Fellow Simulations Plus, Inc. |
Resources
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Design and tests of prospective property predictions for novel antimalarial 2-aminopropylaminoquinolones
Learn MoreThere is a pressing need to improve the efficiency of drug development, and nowhere is that need more clear than in the case of neglected diseases...
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Design, Synthesis and Testing of Novel Antimalarial Drug Leads Using in silico Tools
Learn MoreThe World Health Organization has estimated that over 200 million people suffered from malaria in 2010 and that over 600,000 people died from it that year [1]. Growing problems with resistance to existing anti…
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Application of Absorption Modeling in Rational Design of Drug Product Under Quality-by-Design Paradigm
Learn MorePhysiologically based absorption models can be an important tool in understanding product performance and hence implementation of Quality by Design (QbD) in drug product development.
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Computer Assisted Drug Design of Tinosporide for treatment of Cancer: a Combined Density Functional and Molecular Docking Study
Learn MoreThis article discusses theory behind the most important methods and recent successful applications of halogen-directed tinosporide, ligand-based methods use only ligand information...
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Computer-Aided Drug Design of DNA Repair Inhibitors Targeting the ERCC1-XPF Endonuclease
Learn MoreComputational methods are nowadays essential tools employed in the field of drug design.
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Computer Aided Drug Discovery (Structure-based and ligand-based design).
Learn MoreDriven by chemistry but increasingly guided by pharmacology and the clinical sciences, drug research has contributed more to the progress of medicine during the past century than any other scientific factor.