Paper
The SAMPL9 host–guest blind challenge: an overview of binding free energy predictive accuracy
We report the results of the SAMPL9 host–guest blind challenge for predicting binding free energies.
Integrating multiscale and machine learning approaches towards the SAMPL9 log P challenge
This work highlights three approaches integrating quantum mechanics, molecular mechanics, and machine learning towards predicting the partition coefficient (log P) as part of the ninth iteration of the SAMPL challenges.
Paper
Prediction of toluene/water partition coefficients of SAMPL9 compounds: comparison of the molecular dynamics force fields GAFF/RESP and GAFF/IPolQ-Mod + LJ-fit
Force field comparison including solvation structure analysis for API compounds.
Host–guest systems for the SAMPL9 blinded prediction challenge: phenothiazine as a privileged scaffold for binding to cyclodextrins
This study uses isothermal titration calorimetry and NMR spectroscopy to characterize 15 phenothiazine-cyclodextrin interactions. It is found that phenothiazine drugs are privileged guests of β–cyclodextrin and its methylated derivatives.
Development and test of highly accurate endpoint free energy methods. 3: partition coefficient prediction using a Poisson–Boltzmann method combined with a solvent accessible surface area model for SAMPL challenges
Apply a Poisson–Boltzmann surface area method for transfer free energy calculations.
Expanded ensemble predictions of absolute binding free energies in the SAMPL9 host–guest challenge
An expanded ensemble (EE) method was deployed in distributed molecular simulations to make blind predictions of host–guest binding affinities in SAMPL9. Results suggest EE can efficiently predict and rank absolute binding free energies.
Blind prediction of toluene/water partition coefficients using COSMO-RS: results from the SAMPL9 challenge
Accurately predicting partition coefficients log P is crucial for reducing costs and accelerating drug design as it provides valuable information about the bioavailability, pharmacokinetics, and toxicity of different drug candidates.
Paper
Energy-entropy multiscale cell correlation method to predict toluene–water log P in the SAMPL9 challenge
The energy-entropy multiscale cell correlation (EE-MCC) method is used to calculate toluene–water log P values of the 16 drug molecules in the SAMPL9 physical properties challenge.
Paper
Taming multiple binding poses in alchemical binding free energy prediction: the β-cyclodextrin host–guest SAMPL9 blinded challenge
The binding free energies of the multiple binding poses of the βCD/phenothiazine host–guest complexes are integrated to form SAMPL9 predictions.
Prediction of toluene/water partition coefficients in the SAMPL9 blind challenge: assessment of machine learning and IEF-PCM/MST continuum solvation models
In recent years the use of partition systems other than the widely used biphasic n-octanol/water has received increased attention to gain insight into the molecular features that dictate the lipophilicity of compounds.
About this collection
The aim of this ongoing collection on the most recent Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges is to report results and lessons learned, with papers from recent participants. The SAMPL challenges test computational models on predictions of properties related to obstacles faced in drug discovery settings.
The SAMPL8 Challenges:
SAMPL8 included components dealing with host-guest binding prediction and physical property prediction (log D and pKa), and this collection includes a series of reports on the challenge.
The SAMPL9 Challenges:
SAMPL9 included components dealing with predicting inhibitors of NanoLuc, host-guest binding prediction, and prediction of toluene-water log P values.
The euroSAMPL1 Challenge:
euroSAMPL1 was devoted to the prediction of aqueous pKa values of small drug-like molecules provided as SMILES strings and peer evaluation of research data “FAIRness”.
This collection is Guest Edited by Stefan Kast (TU Dortmund University), Ricardo Mata (Georg-August-University Göttingen), Paul Czodrowski (Johannes Gutenberg-University Mainz) and David Mobley (University of California, Irvine).