Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain

Thakkar, Amol; Kogej, Thierry; Reymond, Jean‐Louis; Engkvist, Ola; Bjerrum, Esben Jannik

doi:10.1039/c9sc04944d

“…7 As such, it is unlikely that predictions beyond the top 50 templates will be enumerated even if considered due to their large cumulative probability, and there is no guarantee a predicted template can be successfully applied to yield a set of reactants. 7,29 Therefore, this reflects to our measure of comparison outlined previously, the quantity of ring formations in the top 50 templates predicted and their rank. Across the substrates examined ( Figure 2) we found that 'Ring Breaker' was able to suggest a ring-forming template in 98 % of cases using a model trained on Reaxys, compared to 45 % of cases when using the general model (Table 2).…”

Section: Dataset Ringmentioning

confidence: 76%

“…The predictions were made by two models trained on the USPTO or Reaxys data respectively, to determine whether a difference in performance could be observed between the two datasets, considering their differing size and coverage as determined in a previous study. 29 We found that for the 20 substrates tested in this part of the study, 'Ring Breaker' performed better for the prediction of ring formations on average ( Table 2). For each molecule, the top 50 predictions were restricted to those describing ring formations to compare the models.…”

Section: Prediction Of Well-known Ring Formationsmentioning

confidence: 78%

‘Ring Breaker’: Assessing Synthetic Accessibility of the Ring System Chemical Space

Thakkar

¹

,

Selmi²,

Reymond³

et al. 2019

Preprint

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<p>Ring systems in pharmaceuticals, agrochemicals and dyes are ubiquitous chemical motifs. Whilst the synthesis of common ring systems is well described, and novel ring systems can be readily computationally enumerated, the synthetic accessibility of unprecedented ring systems remains a challenge. ‘Ring Breaker’ enables the prediction of ring-forming reactions, for which we have demonstrated its utility on frequently found and unprecedented ring systems, in agreement with literature syntheses. We demonstrate its performance on a range of ring fragments from the ZINC database and highlight its potential for incorporation into computer aided synthesis planning tools. Additionally, we generate a multi-label dataset using bipartite reaction graphs on which we train ‘Ring Breaker’ to model the relationship between one ring fragment and the multiple reactions recorded for its synthesis in the dataset; we thereby overcome the single-label approaches previously used. These approaches to ring formation and retrosynthetic disconnection offer opportunities for chemists to explore and select more efficient syntheses/synthetic routes. </p>

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“…7 As such, it is unlikely that predictions beyond the top 50 templates will be enumerated even if considered due to their large cumulative probability, and there is no guarantee a predicted template can be successfully applied to yield a set of reactants. 7,29 Therefore, this reflects to our measure of comparison outlined previously, the quantity of ring formations in the top 50 templates predicted and their rank. Legend: number of ring formations predicted (rank of first applicable ring formation) e.g.…”

Section: Dataset Ringmentioning

confidence: 76%

“…This is in contrast to our previous observations, where we reported that the ability to generate synthetic routes for the general model did not depend on the training dataset. 29 We have now determined that for the domain specific case of ring formations there is a clear effect arising from the training set used, attributed to the number and diversity of the samples available to the network for training. The performance of the model on ring systems classed as 'rare' in the ZINC database, is surprising ( Figure 6).…”

Section: Prediction Of Fragmentsmentioning

confidence: 99%

‘Ring Breaker’: Assessing Synthetic Accessibility of the Ring System Chemical Space

Thakkar¹,

Selmi²,

Reymond³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

<p>Ring systems in pharmaceuticals, agrochemicals and dyes are ubiquitous chemical motifs. Whilst the synthesis of common ring systems is well described, and novel ring systems can be readily computationally enumerated, the synthetic accessibility of unprecedented ring systems remains a challenge. ‘Ring Breaker’ enables the prediction of ring-forming reactions, for which we have demonstrated its utility on frequently found and unprecedented ring systems, in agreement with literature syntheses. We demonstrate its performance on a range of ring fragments from the ZINC database and highlight its potential for incorporation into computer aided synthesis planning tools. Additionally, we generate a multi-label dataset using bipartite reaction graphs on which we train ‘Ring Breaker’ to model the relationship between one ring fragment and the multiple reactions recorded for its synthesis in the dataset; we thereby overcome the single-label approaches previously used. These approaches to ring formation and retrosynthetic disconnection offer opportunities for chemists to explore and select more efficient syntheses/synthetic routes. </p>

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“…Solving a retrosynthetic problem is equivalent to exploring a directed acyclic graph of all possible retrosyntheses of a given target and finding the optimal route based on the optimization of specific cost functions (price of synthesis, raw materials availability, efficacy, etc.). Monte-Carlo Tree Search (MCTS) algorithms were the method of choice to explore retrosynthetic graphs in previous works [12,35,38]. Here, we use a hypergraph exploration strategy (see Section 4.5).…”

Section: Multi-step Workflowmentioning

confidence: 99%

Predicting Retrosynthetic Pathways Using a Combined Linguistic Model and Hyper-Graph Exploration Strategy

Schwaller

¹

,

Petraglia²,

Zullo³

et al. 2019

Preprint

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We present an extension of our Molecular Transformer architecture combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention. The single-step retrosynthetic model sets a new state of the art for predicting reactants as well as reagents, solvents and catalysts for each retrosynthetic step. We introduce new metrics (coverage, class diversity, round-trip accuracy and Jensen-Shannon divergence) to evaluate the single-step retrosynthetic models, using the forward prediction and a reaction classification model always based on the transformer architecture. The hypergraph is constructed on the fly, and the nodes are filtered and further expanded based on a Bayesian-like probability. We critically assessed the end-to-end framework with several retrosynthesis examples from literature and academic exams. Overall, the frameworks has a very good performance with few weaknesses due to the bias induced during the training process. The use of the newly introduced metrics opens up the possibility to optimize entire retrosynthetic frameworks through focusing on the performance of the single-step model only.

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Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain

Abstract: Computer Assisted Synthesis Planning (CASP), datasets and their performance.

Cited by 150 publications

References 44 publications

‘Ring Breaker’: Assessing Synthetic Accessibility of the Ring System Chemical Space

‘Ring Breaker’: Assessing Synthetic Accessibility of the Ring System Chemical Space

‘Ring Breaker’: Assessing Synthetic Accessibility of the Ring System Chemical Space

Predicting Retrosynthetic Pathways Using a Combined Linguistic Model and Hyper-Graph Exploration Strategy

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