A lot of what you write is to the point and very valid. However, I think you are missing part of the story. Let’s start with
“Unlike drug development, where you’re trying to precisely hit some key molecular mechanism, assessing toxicity almost feels…brutish in nature”
I assume you don’t really believe this. Toxicity is often exactly about precisely hitting some key molecular mechanism. A mechanism that you may have no idea your chemistry is going to hit before hand. A mechanism moreover that you cannot use a straight forward ML to find because your chemistry is not in any training set that an ML model could access. It is very easy to underestimate the vastness of drug-like chemical space, and it is generally the case any given biological target molecule (desired or undesired) can be inhibited or otherwise interfered with a wide range of different chemical moieties (thus keeping medicinal chemists very well employed, and patent lawyers busy). There is unlikely to be toxicological data on any of them unless the target is quite old and there is publically available data on some clinical candidates.
We look to AlphaFold as the great success for ML in the biological chemistry field, and so we should, but we need to remember that AlphaFold is working on an extremely small portion of chemical space, not much more than that covered by the 20 natural amino acids. So AlphaFold’s predictions can be comfortably within distribution of what is already established by structural biology. ML models for toxicology, on the other hand, are very frequently predicting out of distribution.
In point of fact the most promising routes to avoiding toxicity reside in models that are wholly or partially physics-based. If we are targeting a particular kinase (say) we can create models (using AlphaFold if necessary) of all the most important kinases we don’t want to hit and, using physics-based modelling, predict whether we could get unwanted activity against any of these targets. We still have the problem of hitting unrelated protein targets but even here we could, in principle, screen for similarities in binding cavities over a wide range of off-targets and use physics-based modelling to assess cases where there is a close enough match.
Needless to say that requires an awful lot of compute and no-one is really doing this to scale yet. It is a very difficult problem.
I would also expect that the road is through creating models that predict off-target interactions. AlphaFold3 seems to be able to make some predictions about whether or not a given drug will bind with a particular proteins. Those aren’t yet 100 percent accurate but Isomorphic Laboratories probably already does this kind of modeling.
Knowing off-targets itself doesn’t tell you how exactly how serious the side-effects from hitting those off-targets happen to be but it’s quite useful and allows avoiding the most egregious drug candidates that hit the most of targets.
A lot of what you write is to the point and very valid. However, I think you are missing part of the story. Let’s start with
“Unlike drug development, where you’re trying to precisely hit some key molecular mechanism, assessing toxicity almost feels…brutish in nature”
I assume you don’t really believe this. Toxicity is often exactly about precisely hitting some key molecular mechanism. A mechanism that you may have no idea your chemistry is going to hit before hand. A mechanism moreover that you cannot use a straight forward ML to find because your chemistry is not in any training set that an ML model could access. It is very easy to underestimate the vastness of drug-like chemical space, and it is generally the case any given biological target molecule (desired or undesired) can be inhibited or otherwise interfered with a wide range of different chemical moieties (thus keeping medicinal chemists very well employed, and patent lawyers busy). There is unlikely to be toxicological data on any of them unless the target is quite old and there is publically available data on some clinical candidates.
We look to AlphaFold as the great success for ML in the biological chemistry field, and so we should, but we need to remember that AlphaFold is working on an extremely small portion of chemical space, not much more than that covered by the 20 natural amino acids. So AlphaFold’s predictions can be comfortably within distribution of what is already established by structural biology. ML models for toxicology, on the other hand, are very frequently predicting out of distribution.
In point of fact the most promising routes to avoiding toxicity reside in models that are wholly or partially physics-based. If we are targeting a particular kinase (say) we can create models (using AlphaFold if necessary) of all the most important kinases we don’t want to hit and, using physics-based modelling, predict whether we could get unwanted activity against any of these targets. We still have the problem of hitting unrelated protein targets but even here we could, in principle, screen for similarities in binding cavities over a wide range of off-targets and use physics-based modelling to assess cases where there is a close enough match.
Needless to say that requires an awful lot of compute and no-one is really doing this to scale yet. It is a very difficult problem.
I would also expect that the road is through creating models that predict off-target interactions. AlphaFold3 seems to be able to make some predictions about whether or not a given drug will bind with a particular proteins. Those aren’t yet 100 percent accurate but Isomorphic Laboratories probably already does this kind of modeling.
Knowing off-targets itself doesn’t tell you how exactly how serious the side-effects from hitting those off-targets happen to be but it’s quite useful and allows avoiding the most egregious drug candidates that hit the most of targets.