@inbook{b1c4c18f861b41df91e0c783437365ea,
title = "Perspective on the SAMPL and D3R Blind Prediction Challenges for Physics-Based Free Energy Methods",
abstract = "Solvation and binding thermodynamics of a drug-like molecule is quantified by the respective free energy (FE) change that governs physical properties like log P/log D and binding affinities as well as more complex features such as solubility or permeability. The drug discovery process benefits significantly from reliable predictions of FEs, which are hence a key area for the theoretical and modeling community. Despite the clear physical background rooted in statistical mechanics, the desired accuracy goal is hard to achieve. Current modeling methods still need to be improved in various areas related to the FE problem, such as the quality of force fields and quantum-mechanical approximations, the efficiency of sampling algorithms as well as the robustness of computational workflows. In this context, blind prediction challenges, where participants are tasked with testing their computational methods and workflows on compound property predictions without knowing the experimental data, are excellent testbeds to evaluate and improve the modeling methodology. SAMPL (Statistical Assessment of the Modeling of Proteins and Ligands) and Drug Design Data Resource-Grand Challenges (D3R-GCs) represent widely known initiatives demonstrating how the “blind prediction” concept boosts the development of FE predictions. In this chapter, we summarize the status of recent SAMPL and D3R-GCs from the point of view of long-time participants, with the aim of providing the community with a collection of datasets and references.",
author = "Nicolas Tielker and Lukas Eberlein and Oliver Beckstein and Stefan G{\"u}ssregen and Iorga, {Bogdan I.} and Kast, {Stefan M.} and Shuai Liu",
note = "Funding Information: D3R was funded by NIH through the years 2014 to 2019 and the project was located at the University of California San Diego (UCSD) where it was co-directed by Rommie Amaro and Michael Gilson. For D3R, we will concentrate on GCs, and only briefly mention the Continuous Evaluation of Ligand Protein Prediction (CELPP), a weekly organized pose prediction challenge, and other tasks that were solely focusing on pose predictions within GCs. The first GC was named GC2015, where 2015 indicates the year of the challenge. Subsequent GCs were named sequentially. In this chapter, for convenience, we denote GC1 to mean GC2015. A summary of SAMPL and D3R challenges is collected in (), and we will break down the discussions based on the type of properties listed in (). With the help of the organizers of the previous challenges we collected input data from past challenges in publicly accessible GitHub repositories as indicated in the column Challenge date in (); these data are made available under a permissive license. Funding Information: OB was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R01GM118772. BII was supported by the Laboratory of Excellence in Research on Medication and Innovative Therapeutics (LERMIT, grant ANR-10-LABX-33) and by the Joint Programming Initiative on Antimicrobial Resistance (JPIAMR, grant ANR-14-JAMR-002). SMK was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany{\textquoteright}s Excellence Strategy – EXC-2033 – Projektnummer 390677874, and under the Research Unit FOR 1979. Funding Information: Beginning with SAMPL, currently funded by NIH and organized by David L. Mobley (UC Irvine) with co-investigators John D. Chodera (MSKCC), Bruce C. Gibb (Tulane), and Lyle Isaacs (Maryland), the first challenge was organized by a research group at Stanford University and scientists at OpenEye Scientific Software. Previous organizers also included several other academic researchers (). Especially J. Peter Guthrie (Western University) played a key role in curating SFE data from the literature, which made the first few SAMPL challenges possible. As SAMPL7 recently completed and SAMPL8 is ongoing, we will focus our discussions here on the fully disclosed challenges SAMPL0-6 while including currently available results for SAMPL7. Publisher Copyright: {\textcopyright} 2021 American Chemical Society. All rights reserved.",
year = "2021",
doi = "10.1021/bk-2021-1397.ch003",
language = "English (US)",
series = "ACS Symposium Series",
publisher = "American Chemical Society",
pages = "67--107",
editor = "Armacost, {Kira A.} and Thompson, {David C.}",
booktitle = "ACS Symposium Series",
}