Molecular Design: Comparing ML models in small molecule drug discovery

Tuesday, 21 April 2026

Comparing ML models in small molecule drug discovery

To start the post I'll share a photo that I took in 2012 of incense sticks at the Truc Lam pagoda near Da Lat. Not long after taking this photo I lost a lens cap (although thankfully not the lens) riding a luge through a forest and would later visit a cricket farm (this was particularly welcome because I had developed a taste for fried crickets during a visit to Cambodia in 2005).

I’ll be reviewing A2025 (Practically Significant Method Comparison Protocols for Machine Learning in Small Molecule Drug Discovery) in this post. I consider the issues addressed by the authors to be extremely important and I think that the credibility of the Machine Learning (ML) field would be greatly enhanced if Editors declared words like 'outperform' to be verboten in manuscripts submitted to their journals. However, I will make a couple of criticisms of the study. First, ML modellers need to properly account for the number of adjustable parameters used to fit training data (the S2006 study goes further than this by arguing that one should also account for size of the descriptor pool). Second, ML modellers need to recognize that cross-validation can make optimistic assessments of model quality when there is high degree of clustering in training data. I’ll point you toward earlier Molecular design blog posts (Sep2024 | Oct2024 | Jul2025) that may be relevant to the discussion. As is usual for posts here at Molecular Design quoted text is indented with my comments italicised in red.<br>The ML models that form the focus of the A2025 study aim to predict properties (more generally behaviour) of compounds from their chemical structures. Although there is currently a lot of hype around ML models for drug discovery it’s worth bearing mind that people have been building quantitative structure-activity/property (QSAR/QSPR) models for decades (the inaugural EuroQSAR conference was held in Prague a mere five years after Czechoslovakia had been invaded by forces from the Soviet Union, the Polish People's Republic, the People's Republic of Bulgaria, and the Hungarian People's Republic). As I see it QSAR/QSPR approaches never really made much of a splash in real world drug discovery and my challenge to those who tout ML models as a panacea for the ills of Pharma/Biotech would be to ask why they think it’s going to be any different this time.<br>One of the difficulties that QSAR/QSPR practitioners faced when working within drug discovery project teams was that projects had often delivered (or had been put out of their misery) by the time there was enough data to build predictively useful models. It’s also worth pointing out that drug discovery teams have frequently delivered (and continue to deliver) clinical development candidates without ever having sufficient data for building usefully predictive QSAR/QSPR models. Something that that many QSAR/QSPR practitioners never seemed to get is that much drug design is actually hypothesis-driven (I discussed this point 16 years ago in K2009 and I’ll point you to the P2012 article by former colleagues). A significant part of hypothesis-driven drug design is identification of exploitable features in structure activity/property relationships (SARs/SPRs) such as activity cliffs and instances of increased polarity not resulting in loss of potency. A simple plot of potency against lipophilicity might not be predictively useful but it can be still used to quantify the extent to the potency of the compound beats the trend in the data (see ‘Alternatives to ligand efficiency for normalization of affinity’ section in NoLE). My view is that hypothesis-driven drug design actually fits very naturally into an AI framework and those who tout AI as a drug design panacea appear to be missing a trick by seeing drug design as essentially an exercise in prediction.<br>Many of the properties of compounds of interest to ML modellers in drug discovery can be modelled as if they are equilibrium constants or rate constants (continuous-valued, dimensioned quantities) and typically fall into three general categories:

In vitro bioactivity is usually quantified in terms of potency (concentration at which a compound exhibits a specified effect in bioactivity assay) and, despite the views expressed in a rather bizarre JMC Editorial (a recent JMC Perspective provides a useful counterview and this blog post is also relevant), is the most important of the properties because you can’t compensate for inadequate potency by increasing quality of compounds or by making them more beautiful (see B2012) and I touch on this point in a recent blog post. It is important that ML modellers be aware that for some ‘new’ modalities such as irreversible covalent inhibition and targeted protein degradation the effect of a compound on the target depends on time as well as concentration. I discuss some of the...