Simulations Plus

This tutorial introduces the built-in 3D capabilities of ADMET Predictor®. Learn how to generate single 3D conformers for property prediction, use the MMFF94S force field for energy minimization, and link external visualization tools like Avogadro to inspect molecular geometries.

This tutorial introduces the PI-ADMET Predictor module. Using a Jupyter Notebook environment, it demonstrates how to combine ADMET Predictor’s powerful algorithms with the Python ecosystem for tasks like SMILES standardization, property calculation via REST API, and metabolite visualization.

This second part of the command line series dives into advanced HTTPK simulation options, including generating CP time curves and utilizing custom .hia parameter files. It also provides a comprehensive guide to Linux installation, licensing with Flexera, and executing shell scripts for automated property predictions.

This tutorial introduces the Command Line Interface (CLI) for ADMET Predictor® 12. Learn how to use essential flags like -t for input types and -n for multi-threaded processing, and explore specialized workflows for medicinal chemists, physical chemists, and computational scientists.

This second part of the REST API series demonstrates how different client applications—from Spotfire to Python scripts—interact with the ADMET Predictor REST API. Watch real-time examples of HTTP requests and responses, including property predictions, image generation, and pharmacokinetic simulations.

This first part of a two-video series explains how to set up the ADMET Predictor REST API server. Follow the step-by-step process for installing the Windows service, configuring license server paths, and managing support folders for seamless property predictions in third-party tools.

This tutorial by Simulations Plus focuses on the Transporters Module in ADMET Predictor®. Understand the role of transporters in drug exposure and toxicity, and learn how to use the 24 built-in substrate and inhibitor models to screen compound libraries.

This tutorial covers the comprehensive Toxicity Module in ADMET Predictor®. Learn about hERG channel inhibition, Ames mutagenicity strains, and how these individual models contribute to the overall Tox Risk score.

In this second part of the Cheminformatics series, Simulations Plus demonstrates how to utilize pre-built CQF (Compound Query Files) for drug-like filtering and toxicophore detection. Learn to customize these files using SMARTS syntax to create personalized structural filters, such as Michael acceptor or PAINS filters.

In this first part of the Cheminformatics series, we explore core tools for chemical space exploration. Learn how to perform explicit substructure searches and advanced Markush queries to categorize large datasets by molecular features.

This tutorial by Simulations Plus introduces the Artificial Intelligence Drug Design (AIDD) module. Discover how to optimize lead compounds for potency, synthetic feasibility, and ADMET properties using multi-objective Pareto optimization.

This comprehensive tutorial by Simulations Plus demonstrates the workflow for building custom machine learning models using ADMET Modeler™. Learn how to prepare data, select descriptors, set up artificial neural network (ANN) ensembles, and validate models with external test sets

In the final part of the HTPK series, Simulations Plus demonstrates how to generate Concentration-Plasma (CP) time profiles for multiple compounds and doses. This tutorial also covers Dose Estimation to meet target plasma concentrations and explores advanced physiological and formulation settings.

In part two of the HTPK series, we explore the Parameter Sensitivity Analysis (PSA) tool to determine whether fraction absorbed is limited by solubility or permeability. The video also demonstrates how to increase simulation accuracy by integrating experimental data alongside in silico predictions

In this first part of the HTPK series, we demonstrate how to run high-throughput pharmacokinetic (HTPK) simulations directly from chemical structures. Learn to calculate fraction absorbed (%FA), fraction bioavailable (%FB), and key PK parameters like Cmax, Tmax, and clearance across multiple doses.