QikProp

QikProp, developed by Schrödinger, is an advanced computational tool designed for the rapid prediction of pharmacokinetic and physicochemical (ADME) properties of small organic molecules. It operates based on the full 3D molecular structure, making it a powerful resource for medicinal chemists and drug discovery teams seeking to evaluate drug candidates early in the development pipeline.

Approximately 40% of drug candidates fail in clinical trials due to poor ADME — absorption, distribution, metabolism, and excretion — properties, resulting in significant wasted time and resources. QikProp addresses this challenge by enabling accurate ADME predictions prior to costly experimental procedures such as high-throughput screening (HTS), filtering out compounds unlikely to succeed and refining lead optimization efforts to improve the overall quality of candidates advancing through development.

Key Capabilities

• Wide range of predicted properties: QikProp predicts the broadest variety of pharmaceutically relevant properties available, including 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, enabling thorough analysis of a molecule's suitability.
• Accurate ADME predictions for novel scaffolds: QikProp delivers equally accurate results for molecules with novel scaffolds as it does for analogs of well-known drugs, ensuring reliable predictions across diverse chemical space.
• Compound library screening: The tool rapidly screens compound libraries for hits, filtering out candidates with unsuitable ADME properties and identifying and prioritizing the most promising compounds for further development.
• Physical descriptors for improved accuracy: QikProp computes over twenty physical descriptors that can be used to improve predictions by fitting to additional or proprietary experimental data, and to generate alternate QSAR models.

Impact on Drug Development

• Early identification of problematic ADME profiles reduces wasted resources and development costs.
• Incorporating ADME predictions into the development workflow generates lead compounds with significantly higher chances of success in clinical trials.
• Supports lead optimization by highlighting and improving desired compound properties before committing to expensive experimental testing.

Training and Resources

• Comprehensive documentation and tutorials are available to help users learn best practices for deploying QikProp within their research projects.
• Online, self-paced certification courses covering molecular modeling topics are offered, with access to Schrödinger software and support included.
• Additional resources include quick start guides, videos, software updates and releases, and dedicated technical support.

QikProp is part of Schrödinger's broader suite of life science computational tools and is complemented by related technologies such as FEP+ for high-performance free energy calculations. It is supported by an extensive body of peer-reviewed publications demonstrating its application across a wide range of drug discovery programs.