Simulations Plus

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.

In the second part of the MedChem Studio™ series, we demonstrate how to evaluate compound subsets and optimize virtual libraries. This tutorial covers distribution analysis to compare virtual vs. exemplified compounds and introduces matched molecular pair (MMP) analysis for property cliff detection.

In this first installment of the MedChem Studio™ series, we demonstrate powerful clustering and group analysis tools. Learn how to organize large data sets into chemotypes, align scaffolds for visual comparison, and perform R-group decomposition to identify SAR trends

In this video, we explore advanced tools for predicting drug metabolites across major enzyme classes, including Cytochrome P450 (SIP), Aldehyde Oxidase (AOX), and UGTs. You will learn how to identify atomic sites of metabolism, interpret scoring systems, and visualize structural transformations within MedChem Designer™.

In this video, we provide a detailed overview of the Model Editor, explaining how to manage prediction modules and interpret applicability domains. Key topics include descriptor sensitivity analysis (DSA), structure sensitivity analysis (SSA), and regression uncertainty.