Paper:
Automatic Chemical Design provides a framework for generating novel molecules with optimized molecular properties. The current model suffers from the pathology that it tends to produce invalid molecular structures. By reformulating the search procedure as a constrained Bayesian optimization problem, we showcase improvements in both the validity and quality of the generated molecules. We demonstrate that the model consistently produces novel molecules ranking above the 90th percentile of the distribution over training set scores across a range of objective functions. Importantly, our method suffers no degradation in the complexity or the diversity of the generated molecules.

Review:
The authors proposed a new method improving a previous Bayesian optimization approach for chemical design (Gomez-Bombarelli et al., 2016b) by addressing the problem that data points need to have valid molecular structures. The main contribution is a constrained Bayesian optimization approach that take into account the constraint on the probability of being valid.
My biggest concern of this paper is that it is not really using sequential evaluations to do automatic design of experiments on molecules. The experiments do not seem to fully support the proposed approach in terms of being able to adaptively do sequential evaluations. 
Detailed comments:
1. The term inside the expectation of the EI criterion in Sec 2.5 should be max(0, f(m)-\eta) rather than max(0, \eta - f(m)).
2. The EIC criterion the authors adopted uses Pr(C(m)) if the constraint is violated everywhere with high probability. It seems like Pr(C(m)) does not have the ability to explore regions with high uncertainty. How does this approach compare to Bayesian level set estimation approaches like 
B. Bryan, R. C. Nichol, C. R. Genovese, J. Schneider, C. J. Miller, and L. Wasserman, “Active learning for identifying function threshold boundaries,” in NIPS, 2006 
I. Bogunovic, J. Scarlett, A. Krause, and V. Cevher, “Truncated variance reduction: A unified approach to bayesian optimization and level-set estimation,” in NIPS, 2016.
3. It would be good to explain in more detail how a constraint is labeled to be valid or invalid. 
4. What is the space of m in Sec 2.3 and the space of m in Sec 2.4? It seems like there is a discrepancy: Sec 2.3 is talking about m as a molecule but Sec 2.4 describes f as a function on the latent variable? It would be good to be clear about it.
5. In the experiment, I think it is very necessary to show the effectiveness of the constrained BO approach in terms of how the performance varies as a function of the number of evaluations on the constraint and the function. The current empirical results only support the claim that the constrained BO approach is able to output more valid latent variables and the function values from constrained BO is higher than vanilla BO under the same number of training data. It is also strange why there is a set of test data. 
Typos/format:
1. citation format problems across the paper, e.g. 
"Gomez-Bombarelli et al. (Gomez-Bombarelli et al., 2016b) presented Automatic Chem"
"SMILES strings Weininger (1988) are a means of representing molecules as a character sequence."
It's likely a problem of misuse of \cite, \citep.
2. no period at the end of Sec 2.4