background pattern

QikProp

Rapid ADME predictions of drug candidates

Enhancing drug development with ADME properties prediction

QikProp is an advanced tool for predicting pharmacokinetic and physicochemical (ADME) properties of small organic molecules based on the full 3D molecular structure.

Approximately 40% of drug candidates fail in clinical trials due to poor ADME (absorption, distribution, metabolism, and excretion) properties, leading to soaring development costs. Early detection of problematic candidates can significantly reduce wasted time and resources.

Accurate ADME prediction, prior to costly experimental procedures like HTS, eliminates unnecessary testing on destined-to-fail compounds. It also refines lead optimization efforts, improving desired compound properties. Incorporating ADME predictions into development generates lead compounds with significantly  higher chances of success in clinical trials.

Key Capabilities

Check mark icon
Wide range of predicted properties

Predicts the widest variety of pharmaceutically relevant properties – octanol/water and water/gas log Ps, log S, log BB, overall CNS activity, Caco-2 and MDCK cell permeabilities, log Khsa for human serum albumin binding, and log IC50 for HERG K+-channel blockage – so that decisions about a molecule’s suitability can be made based on a thorough analysis.

Check mark icon
Accurate ADME properties

Provides equally accurate results in predicting properties for molecules with novel scaffolds as for analogs of well-known drugs.

Check mark icon
Exploring better hits

Rapidly screens compound libraries for hits and filters out candidates with unsuitable ADME properties, identifying and prioritizing the most promising ones. 

Check mark icon
Improving accuracy

Computes over twenty physical descriptors, which can be used to improve predictions by fitting to additional or proprietary experimental data, and to generate alternate QSAR models.

Related Products

Learn more about the related computational technologies available to progress your research projects.

FEP+

High-performance free energy calculations for drug discovery

Publications

Browse the list of peer-reviewed publications using Schrödinger technology in related application areas.

Life Science
Intense bitterness of molecules: Machine learning for expediting drug discovery
Materials Science
Bitter or not? BitterPredict, a tool for predicting taste from chemical structure
Life Science
Search for Non-Nucleoside Inhibitors of HIV-1 Reverse Transcriptase Using Chemical Similarity, Molecular Docking, and MM-GB/SA Scoring
Life Science
Dihydropyridopyrazinones and Dihydropteridinones as Corticotropin-Releasing Factor-1 Receptor Antagonists: Structure-Activity Relationships and Computational Modeling
Life Science
Computer-Aided Design of Non-Nucleoside Inhibitors of HIV-1 Reverse Transcriptase
Life Science
Solution-Phase Synthesis of a Tricyclic Pyrrole-2-Carboxamide Discovery Library Applying a Stetter-Paal-Knorr Reaction Sequence
Life Science
Influence of Molecular Flexibility and Polar Surface Area Metrics on Oral Bioavailability in the Rat
Life Science
QSAR Studies of PC-3 Cell Line Inhibition Activity of TSA and SAHA-like Hydroxamic Acids
Life Science
Prediction of in vitro metabolic stability of calcitriol analogs by QSAR
Life Science
Prediction of Drug Solubility from Structure

Training & Resources

Online certification courses

Level up your skill set with hands-on, online molecular modeling courses. These self-paced courses cover a range of scientific topics and include access to Schrödinger software and support.

Tutorials

Learn how to deploy the technology and best practices of Schrödinger software for your project success. Find training resources, tutorials, quick start guides, videos, and more.