In-silico protein-ligand binding prediction is an ongoing area of research in computational chemistry and machine learning based drug discovery, as an accurate predictive model could greatly reduce the time and resources necessary for the detection and prioritization of possible drug candidates. Proteochemometric modeling (PCM) attempts to make an accurate model of the protein-ligand interaction space by combining explicit protein and ligand descriptors. This requires the creation of information-rich, uniform and computer interpretable representations of proteins and ligands. Previous work in PCM modeling relies on pre-defined, handcrafted feature extraction methods, and many methods use protein descriptors that require alignment or are otherwise specific to a particular group of related proteins. However, recent advances in representation learning have shown that unsupervised machine learning can be used to generate embeddings which outperform complex, human-engineered representations. We apply this reasoning to propose a novel proteochemometric modeling methodology which, for the first time, uses embeddings generated via unsupervised representation learning for both the protein and ligand descriptors. We evaluate performance on various splits of a benchmark dataset, including a challenging split that tests the model's ability to generalize to proteins for which bioactivity data is greatly limited, and we find that our method consistently outperforms state-of-the-art methods.
The authors present a model with state-of-the-art performance for predicting protein-ligand affinity and provide a thorough set of benchmarks to illustrate the superiority of combining learned low-dimensional embedding representations  of both ligands and proteins. The authors then show that these learned representations are more powerful than handcrafted features such as circular fingerprints, etc. when combined into a model that jointly takes as input both the ligand and protein.
I originally suggested to accept the paper, but agree with the other reviewers that the novelty of the work in this paper likely doesn't meet the bar for acceptance given that the most significant contributions of this paper are around combining good ideas from other papers without much additional novelty.
Major comments
Unless I'm misunderstanding the naming conventions used in Table 1, it seems like an omission not to include the performance of DeepPCM+HP+HC in Table 1 to more fully decouple the performance due to the DeepPCM architecture versus the contribution due to using both unsupervised descriptors in combination.
I might expect the performance of the DeepPCM + HP + HC model (not currently shown unless I'm missing it) to exceed the performance of the NIB + HP + HC model based upon the reasoning given in the discussion (i.e., usefulness of joint input training), even if the individual representations were inferior to the unsupervised counterparts.
Since the primary point of the paper is to illustrate the power of combining the unsupervised representations, I'm surprised the aforementioned performance comparison is not prominent. That is, the performance comparison of DeepPCM+HP+HC versus DeepPCM+UP+UC seems like a very central quantity to present and discuss, but appears to be missing currently.
Minor comments
Table 1 was a bit confusing to me at first, because it appears that UD = UP + UC, and HD = HP + HC, but this wasn't obvious to me initially; I'd change the NIB rows to be NIB + HP + HC and NIB + UP + UC, and then you don't even need to define the UD and HD acronyms, which simplifies the table's cognitive load for me and also makes the relationship between the DeepPCM variants more obvious.
> "On the low-coverage-split, we also find that our method significantly outperforms the benchmark."
It would be good to add the %improvement inline here to parallel the other dataset splits listed just before so that you can directly compare the magnitudes.
> "using unsupervised-learned descriptors than when using handcrafted descriptors"
Would be more compelling if you added the DeepPCM + HC + HP performance to Table 1.
> "All hyperparameter optimization of our model was performed on the temporal split"
I think it would improve the paper if the extent to which various architecture choices were optimized over could be included as well; for example, which types, layer sizes, and depths of network architectures were considered in the hyperparam tuning, and any accompanying justification for these design choices.