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Co-authored-by: Anthony Gitter <agitter@users.noreply.github.com>

content/05.treat.md

@@ -194,7 +194,7 @@ In the past few years, a large number of techniques for the generative modeling
 
 Building off the large amount of work that has already gone into text generation [@arxiv:1308.0850], many generative neural networks for drug design initially represented chemicals with the simplified molecular-input line-entry system (SMILES), a standard string-based representation with characters that represent atoms, bonds, and rings [@tag:Segler2017_drug_design].
 
-The first successful demonstration of a deep learning based approach for molecular optimization occurred in 2016 with the development of a SMILES-to-SMILES autoencoder capable of learning a continuous latent feature space for molecules[@tag:Gomezb2016_automatic].
+The first successful demonstration of a deep learning based approach for molecular optimization occurred in 2016 with the development of a SMILES-to-SMILES autoencoder capable of learning a continuous latent feature space for molecules [@tag:Gomezb2016_automatic].
 In this learned continuous space it is possible to interpolate between molecular structures in a manner that is not possible with discrete (e.g. bit vector or string) features or in symbolic, molecular graph space.
 Even more interesting is that one can perform gradient-based or Bayesian optimization of molecules within this latent space.
 The strategy of constructing simple, continuous features before applying supervised learning techniques is reminiscent of autoencoders trained on high-dimensional EHR data [@tag:BeaulieuJones2016_ehr_encode].
@@ -208,18 +208,19 @@ Both the fine-tuning and reinforcement learning approaches can rediscover known,
 
 Reinforcement learning approaches where operations are performed directly on the molecular graph bypass the need to learn the details of SMILES syntax, allowing the model to focus purely on chemistry.
 Additionally, they seem to require less training data and generate more valid molecules since they are constrained by design only to graph operations which satisfy chemical valiance rules [@tag:Elton_molecular_design_review].
-A reinforcement learning agent developed by Zhou et al. [@doi:10.1038/s41598-019-47148-x] demonstrated superior molecular optimization performance on optimizing  the quantitative estimate of drug-likeness (QED) metric and the "penalized logP" metric (logP minus the synthetic accessibility) when compared with other deep learning based approaches such as the Junction Tree VAE [@arxiv:1802.04364], Objective-Reinforced Generative Adversarial Network [@arxiv:1705.10843], and Graph Convolutional Policy Network [@arxiv:1806.02473].
+A reinforcement learning agent developed by Zhou et al. [@doi:10.1038/s41598-019-47148-x] demonstrated superior molecular optimization performance on optimizing the quantitative estimate of drug-likeness (QED) metric and the "penalized logP" metric (logP minus the synthetic accessibility) when compared with other deep learning based approaches such as the Junction Tree VAE [@arxiv:1802.04364], Objective-Reinforced Generative Adversarial Network [@arxiv:1705.10843], and Graph Convolutional Policy Network [@arxiv:1806.02473].
 As another example, Zhavoronkov et al. used generative tensorial reinforcement learning to discover inhibitors of discoidin domain receptor 1 (DDR1) [@tag:Zhavoronkov2019_drugs].
-In contrast to most previous work, six lead candidates discovered using their approach were synthesized and tested in the lab, with 4/6 achieving some degree of binding to DDR1. One of the molecules was chosen for further testing and showed promising results in a cancer cell line and mouse model [@tag:Zhavoronkov2019_drugs]. 
+In contrast to most previous work, six lead candidates discovered using their approach were synthesized and tested in the lab, with 4/6 achieving some degree of binding to DDR1.
+One of the molecules was chosen for further testing and showed promising results in a cancer cell line and mouse model [@tag:Zhavoronkov2019_drugs]. 
 
-In concluding this section, we want to highlight two areas where work is still needed before AI can bring added value to the existing drug discovery process - novelty and synthesizability.
-The work of Zhavoronkov et al. is a arguably an important milestone and recieved much fanfare in the popular press, but Walters and Murko have presented a more sober assessment, noting that the generated molecule they choose to test in the lab is very similar to an existing drug which was present in their training data [@doi:10.1038/s41587-020-0418-2].
+In concluding this section, we want to highlight two areas where work is still needed before AI can bring added value to the existing drug discovery process---novelty and synthesizability.
+The work of Zhavoronkov et al. is arguably an important milestone and received much fanfare in the popular press, but Walters and Murko have presented a more sober assessment, noting that the generated molecule they choose to test in the lab is very similar to an existing drug that was present in their training data [@doi:10.1038/s41587-020-0418-2].
 Small variations of existing molecules are likely not to be much better and may not be patentable.
 One way to tackle this problem is to add novelty and diversity metrics to the reward function of reinforcement learning based algorithms.
-Novelty should also be taken into account when comparing different models - and thus is included in the proposed GuacaMol benchmark (2019) for accessing generative molecules for molecular design [@doi:10.1021/acs.jcim.8b00839].
+Novelty should also be taken into account when comparing different models---and thus is included in the proposed GuacaMol benchmark (2019) for accessing generative molecules for molecular design [@doi:10.1021/acs.jcim.8b00839].
 The other area which has been pointed to as a key limitation of current approaches is synthesizability [@doi:10.1021/acs.jcim.0c00174; @doi:10.1021/acsmedchemlett.0c00088].
-Current heuristics of synthesizability, such as the synthetic accessibility score, are based on a relatively limited domain of chemical data and are too restrictive, so better models/heuristics of synthesizability should help in this area [doi:10.1021/acs.jcim.0c00174]. 
+Current heuristics of synthesizability, such as the synthetic accessibility score, are based on a relatively limited domain of chemical data and are too restrictive, so better models of synthesizability should help in this area [@doi:10.1021/acs.jcim.0c00174]. 
 
-As noted before, genetic algorithms use hard coded rules based on possible chemical reactions to generate molecular structures and therefore may have less trouble generating synthesizable molecules [@doi:10.1021/acs.jmedchem.5b01849].
-We note in passing that Jensen et al. (2018) [@doi:10.1039/C8SC05372C] and Yoshikawa et al. (2019) [@doi:10.1246/cl.180665] have both demostrated genetic algorithms which are competative with deep learning approaches. 
+As noted before, genetic algorithms use hard-coded rules based on possible chemical reactions to generate molecular structures and therefore may have less trouble generating synthesizable molecules [@doi:10.1021/acs.jmedchem.5b01849].
+We note in passing that Jensen (2018) [@doi:10.1039/C8SC05372C] and Yoshikawa et al. (2019) [@doi:10.1246/cl.180665] have both demonstrated genetic algorithms that are competitive with deep learning approaches. 
 Progress on overcoming both of these shortcomings is proceeding on many fronts, and we believe the future of deep learning for molecular design is quite bright.