Grand Challenges for Predictive Modeling in Small Molecule Drug Discovery

In this blog post I’ll be taking a look at C2026 (Grand Challenges for Predictive Modeling in Small Molecule Drug Discovery) which has been published as a ChemXriv preprint. A well-organized grand challenge can indeed help focus scientific research effort on the most important challenges and I think the authors put it well with their statement:

While there is substantial enthusiasm (particularly around AI) for revolutionizing drug discovery, this moment demands sharper problem definition.

In my view, however, C2026 could have been be better organized (for example, I would question why covalent binding is in DOMAIN: CHEMISTRY while pKa is in DOMAIN: PHARMACOLOGY). Nevertheless, the article is still at the preprint stage and my feedback will hopefully be helpful for the authors.  

I’ll direct readers to a recent blog post (The objectives of drug design) in which I suggest that it can be helpful to see design of drugs in terms of on-target bioactivity (good things that drugs do to the human body), off-target bioactivity (bad things that drugs do the human body) and exposure (things that the human body does to drugs). Uncertainty pervades drug discovery and even if we knew the exact extent to which a targets were engaged in vivo we still wouldn’t know what effects drugs will have on patients in the absence of other information (this is the uncertainty that results from the complexity of biology). One significant source of uncertainty is that we generally can’t currently measure the concentration of a drug at its site(s) of action and I recommend that everybody working in Drug Discovery (and Chemical Biology) take a look at SR2019 (Smith & Rowland, Intracellular and Intraorgan Concentrations of Small Molecule Drugs: Theory, Uncertainties in Infectious Diseases and Oncology, and Promise DMD 2019 47:667-672). 

Some years ago I suggested that drug design could be classified as prediction-driven or hypothesis-driven and I’ll direct readers to an the P2012 article on hypothesis-driven drug design by former colleagues. Back in 2009 I stated that “in many situations, properties of compounds simply cannot be predicted with the accuracy required for meaningful design, especially when optimization is performed against multiple end points” and, despite some impressive advances in predictive chemistry since then, this is still my view. Put another way drug discovery needs to be considered in a Design of Experiments framework and I consider it an error to perceive it as simply an exercise in prediction.

The value of a prediction made using chemical structure as the only input drops sharply once a sample of the compound has been prepared and decisions as to whether further work on an existing compound is justified will invariably be based on measured data. For example, the PK/PD modelling used to set the dose will typically be based on measured bioactivity (often cell-based) and pharmacokinetics. Aside from speed the great advantage of calculating ‘relative’ (see CAS2017), as opposed to ‘absolute’ free energy is that it enables project team scientists to use existing affinity and potency measurements for design. That said, the purpose of grand challenges like these is to articulate what we need to be able to predict rather than get distracted by feasibility issues.

With the preamble out of the way I’ll focus on the grand challenges and for the remainder of the post my comments will follow the order of the manuscript. As noted in my review of A2025, 'molecule' should not be used as a synonym for either 'compound' or 'chemical structure'. 

DOMAIN: CHEMISTRY

I suggest covering Covalent Binding in DOMAIN: STRUCTURE and DOMAIN: ENERGY and would include reactivity in Challenge: Chemical Stability and Degradation Products (a quinone might be perfectly stable but it’s not something that you would want to have in a enzyme inhibition assay). My view is that physicochemical properties such as pKa, aqueous solubility, aggregation and passive permeability would be more appropriately covered in DOMAIN: CHEMISTRY than in DOMAIN: PHARMACOLOGY and I would also include alkane/water partition coefficient (this is more appropriate than its octanol/water equivalent as for studying aqueous solvation and is also a better model for the core of a lipid bilayer). It might also be worth including UV-Vis absorption and fluorescence here given that both phenomena are widely exploited to assay bioactivity of compounds.

DOMAIN: STRUCTURE

Given significant interest in ‘new modalities’ I suggest referring to ‘targets’ rather than ‘proteins’ and it might be worth considering ternary structures (important in targeted protein degradation). Structures for target-ligand complexes are not directly relevant to design when association is irreversible although they are still useful starting points for building transition state models.

DOMAIN: ENERGY

Many of the quantities that form the basis of drug design fit naturally into this domain given that they are effectively equilibrium constants or rate constants. Given significant interest in ‘new modalities’ I suggest referring to ‘targets’ rather than ‘proteins’. For irreversibly-bound ligands it's also necessary to calculate the transition state energy because target engagement occurs under kinetic control. My view is that  oral absorption and drug distribution as well as modelling of enzymatic reactions (for example, oxidative metabolism by CYPs) and active transport could fit naturally into the energy domain.  

DOMAIN: PHARMACOLOGY

With the exception of toxicity (which I suggest could be accommodated within DOMAIN: ENERGY) none of the challenges in DOMAIN: PHARMACOLOGY are actually related to pharmacology.