Evaluation of log\xa0P, pKa, and log\xa0D predictions from the SAMPL7 blind challenge

Teresa Danielle Bergazin,David L Mobley,Karol Francisco,Stefan M Kast,Nicolas Tielker,Junjun Mao,Yingying Zhang,Carlo Ballatore,M R Gunner

doi:10.1007/s10822-021-00397-3

Abstract

The Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pKa for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pKa challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pKa challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pKa values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pKa prediction methods.

Highlights

Computational modeling aims to enable molecular design, property prediction, prediction of biomolecular interactions, and provide a detailed understanding of chemical and biological mechanisms
KF and CB collected a set of measured water-octanol log P, log D, and pKa values for 22 compounds, along with PAMPA permeability values [77]. Since this was our first time hosting a permeability challenge, and these calculations remain challenging for many methods, we did not have enough participants to form meaningful conclusions so the challenge is not discussed in this paper, but we provide a link to the challenge’s GitHub page
There were 33 blind submissions collected from 17 groups (Tables of participants and their predictions can be found in the SAMPL7 GitHub Repository and in the Supporting Information.)

Summary

Introduction

Computational modeling aims to enable molecular design, property prediction, prediction of biomolecular interactions, and provide a detailed understanding of chemical and biological mechanisms. Methods for making these types of predictions can suffer from poor or unpredictable performance, hindering their predictive power. Without a large scale evaluation of methods, it can be difficult to know what method would yield the most accurate predictions for a system of interest. Large scale comparative evaluations of methods are rare and difficult to perform because no individual group has expertise in or access to all relevant methods. By focusing on specific areas in need of improvement, SAMPL helps drive progress in computational modeling

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Results

Conclusion