This Week In Cheminformatics: Issue #031

Equilibrist, DiffDock-Pocket on LIT-PCBA, Ligand Drift Rate Descriptor and a long list of papers

Jul 27, 2026

Highlights

Post : https://butwhyisthat.substack.com/p/ecfp | Code: https://github.com/manas02/ecfp

Equilibrist: A Browser-Based Platform for Fitting Equilibrium and Rate Constants from Optical and NMR Data

Eric Masson published Equilibrist as an open source streamlit web app for fitting equilibrium and rate constants from concentration using NMR, and UV-vis/fluorescence titration data via a plain-text reaction scripting language. The implementation includes 2D RMSE profiling to visualize pairwise parameter correlation, a sloppy-spectrum eigendecomposition to show which parameter combinations the data actually constrain, and many more interesting checks. There are also in built uncertainty estimates using bootstrap, jackknife and monte carlo. Pretty useful alternative to SupraFit. The code is available on Github.

Real-World Assessment of Machine-Learned Docking Using Bioassay-Derived Benchmarks

Ahmed et al. benchmark an ML docking pipeline (DiffDock-Pocket for pose generation, rescored with the Pafnucy 3D-CNN affinity model) against CDOCKER and Surflex-Dock on DUD-E and LIT-PCBA, using AUC and EF1 (enrichment at the top 1% of the ranked list vs. random). On DUD-E, DiffDock-Pocket/Pafnucy and Surflex-Dock performance is similar (AUC 0.70/0.70, EF1 7.64/7.40). CDOCKER wasn't run on LIT-PCBA (too slow), but while comparing DiffDock-Pocket/Pafnucy with Surflex-Dock, they observed both drop in performance (AUC 0.59/0.48, EF1 1.94/1.73). ML workflow's performance margin over Surflex is proportionally larger on LIT-PCBA than on DUD-E. They also show DiffDock-Pocket beats CDOCKER on pose reproduction (79.8% vs. 73.9% of poses within 2Å RMSD, using 40 samples vs. CDOCKER's 500 !), but when both sets of poses are rescored with the same FACTS solvation function, CDOCKER has better AUC. This shows pose accuracy and scoring performance are not correlated. It is a good reminder that DUD-E performance numbers for ML docking methods are mostly upper bounds.

Ligand Drift Rate Descriptor in Receptor Pocket: Molecular Docking Sampling for Differentiating Partial Agonists in Nuclear Receptors

PPARγ has been targeted for type 2 diabetes for quite some time now. This is usually done via full agonists like rosiglitazone, pioglitazone, and troglitazone or partial agonists like TZDs, halofenate, and amorfrutins. It’s been shown that the safety profiles of full agonists is much worse than partial agonists. So, this begs the question what makes a partial agonist, a partial agonist. In this paper, Lin et al. try to answer this question and show that their “Ligand Drift Rate Descriptor“ correlates with efficacy classes. From earlier works using NMR, MD, etc. studies it’s shown that these partial agonists might bind beyond the canonical binding site and might sample multiple conformations and binding sites within the pocket. The authors took this inspiration and docked each ligand 100 times, took the top 20 poses from each and counted how many of them fail to stay within a H-bond distance of some key residues that define the canonical binding region. This descriptor is what they called Ligand Drift Rate Descriptor. With some parameter tweaking, using GOLD’s GA, pose diversity of ~0.5 Å RMSD, and receptor flexibility enabled, at a 10 Å H-bond displacement threshold, the Ligand Drift Rate was able to separate full agonists from partial agonists in both training and ChEMBL validation set (N=1079). They show that partial agonists consistently drifted more than full agonists across essentially all tested configurations, supporting their two-state Boltzmann-like model of ligand occupancy. They did note however that choice in docking algorithm, pocket structure, H-bond displacement threshold, retained pose count (20 vs more) matters a lot and changing it might yield unexpected results.

Long List

Cheminformatics

MedChem

Other

Palate Cleanser

Did anyone notice how horrible the typesetting (on web) has become since the latest ACS website migration. Also what’s up with all these migrations first RCS now ACS. Reminds me of this XKCD.