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Overview
  QikProp: Rapid ADME predictions of drug candidates
QikProp efficiently evaluates pharmaceutically relevant properties for over half a million compounds per hour, making it an indispensable lead generation and lead optimization tool.


The Advantages of ADME Properties Prediction

Nearly 40% of drug candidates fail in clinical trials due to poor ADME (absorption, distribution, metabolism, and excretion) properties. These late-stage failures contribute significantly to the rapidly escalating cost of new drug development. The ability to detect problematic candidates early can dramatically reduce the amount of wasted time and resources, and streamline the overall development process.

Accurate prediction of ADME properties prior to expensive experimental procedures, such as HTS, can eliminate unnecessary testing on compounds that will ultimately fail; ADME prediction can also be used to focus lead optimization efforts to enhance the desired properties of a given compound. Finally, incorporating ADME predictions as a part of the development process can generate lead compounds that are more likely to exhibit satisfactory ADME performances during clinical trials.

QikProp-predicted water/octanol partition coefficients are plotted against experiment.  Excellent agreement (r<sup>2</sup> = 0.93, and RMSD = 0.49) is seen for this predicted property across over four hundred pharmaceutically relevant compounds.  Similar agreement is seen for all QikProp predicted properties.
QikProp-predicted water/octanol partition coefficients are plotted against experiment. Excellent agreement (r2 = 0.93, and RMSD = 0.49) is seen for this predicted property across over four hundred pharmaceutically relevant compounds. Similar agreement is seen for all QikProp predicted properties.
   Features
 
Wide range of predicted properties: QikProp 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.
Accurate ADME properties: QikProp bases its predictions on the full 3D molecular structure; unlike fragment-based approaches, QikProp can provide equally accurate results in predicting properties for molecules with novel scaffolds as for analogs of well-known drugs.
Lead generation: QikProp rapidly screens compound libraries for hits. QikProp identifies molecules with computed properties that fall outside the normal range of known drugs, making it simple to filter out candidates with unsuitable ADME properties.
Lead optimization: QikProp can play an important role during lead optimization by analyzing similarity within a class of compounds as well as by identifying compounds to avoid because they exhibit extreme values of predicted properties.
Improving accuracy: QikProp 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.
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