Optimization of an in Silico Protocol Using Probe Permeabilities to Identify Membrane Pan-Assay Interference...

Area of Science:

• Computational chemistry
• Drug discovery

Background:

• Membrane pan-assay interference compounds (PAINS) can lead to false positives in drug discovery.
• Early identification of PAINS is crucial to prevent wasted resources.

Purpose of the Study:

• To optimize an in silico protocol for discriminating compounds with different membrane PAINS behaviors.
• To establish a reliable computational method for identifying membrane PAINS.

Main Methods:

• Utilized umbrella sampling (US) and molecular dynamics (MD) simulations.
• Optimized US parameters including reference position, number of umbrellas, and strength.
• Employed the inhomogeneous solubility-diffusion model (ISDM) to calculate membrane permeability.

Main Results:

• The optimized protocol effectively discriminates between compounds with varying membrane PAINS characteristics.
• Adjusting the US reference position mitigated sensitivity to membrane thickness fluctuations.
• Resveratrol and curcumin showed distinct membrane PAINS behavior, and nothofagin was reclassified.

Conclusions:

• Presented a computationally efficient and accurate in silico protocol for identifying membrane PAINS.
• The proposed method can serve as a future reference standard for PAINS detection.
• This approach aids in avoiding false leads and optimizing drug discovery pipelines.