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

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.

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‘Ring Breaker’: Neural Network Driven Synthesis Prediction of the Ring System Chemical Space

10.26434/chemrxiv.9938969.v3 ◽

2020 ◽

Author(s):

Amol Thakkar ◽

Nidhal Selmi ◽

Jean-Louis Reymond ◽

Ola Engkvist ◽

Esben Jannik Bjerrum

Keyword(s):

Neural Network ◽

Chemical Space ◽

Ring System ◽

Data Driven ◽

Ring Systems ◽

The Neural Network ◽

Synthetic Accessibility ◽

Synthesis Planning ◽

Data Driven Approach ◽

Synthetic Routes

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’ uses a data-driven approach to enable 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 the performance of the neural network on a range of ring fragments from the ZINC and DrugBank databases and highlight its potential for incorporation into computer aided synthesis planning tools. These approaches to ring formation and retrosynthetic disconnection offer opportunities for chemists to explore and select more efficient syntheses/synthetic routes.

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‘Ring Breaker’: Neural Network Driven Synthesis Prediction of the Ring System Chemical Space

10.26434/chemrxiv.9938969 ◽

2020 ◽

Author(s):

Amol Thakkar ◽

Nidhal Selmi ◽

Jean-Louis Reymond ◽

Ola Engkvist ◽

Esben Jannik Bjerrum

Keyword(s):

Neural Network ◽

Chemical Space ◽

Ring System ◽

Data Driven ◽

Ring Systems ◽

The Neural Network ◽

Synthetic Accessibility ◽

Synthesis Planning ◽

Data Driven Approach ◽

Synthetic Routes

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’ uses a data-driven approach to enable 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 the performance of the neural network on a range of ring fragments from the ZINC and DrugBank databases and highlight its potential for incorporation into computer aided synthesis planning tools. These approaches to ring formation and retrosynthetic disconnection offer opportunities for chemists to explore and select more efficient syntheses/synthetic routes.

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Repurposing of FDA approved ring systems through bi-directional target-ring system dual screening

Scientific Reports ◽

10.1038/s41598-020-78077-9 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Surendra Kumar ◽

Cheongyun Jang ◽

Lalita Subedi ◽

Sun Yeou Kim ◽

Mi-hyun Kim

Keyword(s):

Therapeutic Potential ◽

Chemical Space ◽

Drug Repurposing ◽

Ring System ◽

Ring Systems ◽

Accessible Information ◽

Complementary Approach ◽

Clinical Drugs ◽

Approved Drugs ◽

Fda Approved Drugs

AbstractIn drug repurposing approaches, the chemically diverse and potentially safe molecules can be explored as therapeutic potential beyond those originally targeted indications. However, accessible information on a limited number of drug pipelines can lead to competitive over-heating issues, and intellectual property rights also restrict the free investigation in chemical space. As a complementary approach to the drawbacks, ring systems of approved drugs (instead of clinical drugs) can be optimized and used for repurposing purposes. In this study, bi-directional target (T) and ring system (R) dual screening (TR screening) was developed for the repurposing of their rarely used ring systems from FDA approved drugs. The TR screening suggested RAR β and cyproheptadine as the best pair of target and ring system to escape a saddle point. The selected ring system was virtually grown and elaborated with the defined criteria: synthesizability, drug-likeness, and docking pose showing the top scores. The achieved compounds were synthesized and biologically tested with an acceptable ADME/T profile.

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Stochasting modeling of planetary ring systems

International Astronomical Union Colloquium ◽

10.1017/s025292110010154x ◽

1984 ◽

Vol 75 ◽

pp. 461-469 ◽

Cited By ~ 1

Author(s):

Robert W. Hart

Keyword(s):

Maximum Entropy ◽

Ring System ◽

The Sun ◽

Ultimate State ◽

Interparticle Interactions ◽

Ring Systems ◽

First Order ◽

Planetary Ring ◽

Insight Into

ABSTRACTThis paper models maximum entropy configurations of idealized gravitational ring systems. Such configurations are of interest because systems generally evolve toward an ultimate state of maximum randomness. For simplicity, attention is confined to ultimate states for which interparticle interactions are no longer of first order importance. The planets, in their orbits about the sun, are one example of such a ring system. The extent to which the present approximation yields insight into ring systems such as Saturn's is explored briefly.

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Synthesis of Pyrazolofuropyrazine via One-Pot SNAr Reaction and Intramolecular Direct C–H Arylation

Synthesis ◽

10.1055/s-0036-1591885 ◽

2018 ◽

Vol 50 (07) ◽

pp. 1493-1498 ◽

Cited By ~ 3

Author(s):

Shinichiro Fuse ◽

Hiroyuki Nakamura ◽

Megumi Inaba ◽

Shinichi Sato ◽

Manjusha Joshi

Keyword(s):

Drug Discovery ◽

Rapid Development ◽

Ring System ◽

Biologically Active ◽

Drug Candidate ◽

One Pot ◽

Ring Systems ◽

Drug Candidates ◽

Fused Ring ◽

Snar Reaction

Fused-ring systems containing heterocycles are attractive templates for drug discovery. Biologically active 6-5-5+6 fused-ring systems that possess heterocycles are available, but these require a relatively large number of synthetic steps for preparation. Therefore, pyrazolofuropyrazine was designed as a 6-5-5+6 ring system template that incorporates ready accessibility for drug discovery. Pyrazolofuropyrazines were successfully constructed in only a few steps via one-pot SNAr reaction/intramolecular C–H direct arylation. As a drug candidate, pyrazolofuropyrazine has earned a favorable LogP, although significant biological activity has yet to be established; the ready accessibility of pyrazolofuropyrazine template, however, offers an opportunity for the rapid development of promising new drug candidates.

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Retrosynthetic Accessibility Score (RAscore) - Rapid Machine Learned Synthesizability Classification from AI Driven Retrosynthetic Planning

10.26434/chemrxiv.13019993.v1 ◽

2020 ◽

Author(s):

Amol Thakkar ◽

Veronika Chadimova ◽

Esben Jannik Bjerrum ◽

Ola Engkvist ◽

Jean-Louis Reymond

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Virtual Screening ◽

Generative Models ◽

Synthetic Route ◽

Synthetic Accessibility ◽

Wide Range ◽

Computer Aided ◽

Synthesis Planning ◽

Retrosynthetic Analysis

Computer aided synthesis planning (CASP) is part of a suite of artificial intelligence (AI) based tools that are able to propose synthesis to a wide range of compounds. However, at present they are too slow to be used to screen the synthetic feasibility of millions of generated or enumerated compounds before identification of potential bioactivity by virtual screening (VS) workflows. Herein we report a machine learning (ML) based method capable of classifying whether a synthetic route can be identified for a particular compound or not by the CASP tool AiZynthFinder. The resulting ML models return a retrosynthetic accessibility score (RAscore) of any molecule of interest, and computes 4,500 times faster than retrosynthetic analysis performed by the underlying CASP tool. The RAscore should be useful for the pre-screening millions of virtual molecules from enumerated databases or generative models for synthetic accessibility and produce higher quality databases for virtual screening of biological activity.

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Datasets and Their Influence on the Development of Computer Assisted Synthesis Planning Tools in the Pharmaceutical Domain

10.26434/chemrxiv.9897692 ◽

2019 ◽

Author(s):

Amol Thakkar ◽

Thierry Kogej ◽

Jean-Louis Reymond ◽

Ola Engkvist ◽

Esben Jannik Bjerrum

Keyword(s):

Model Performance ◽

Computer Assisted ◽

Patent Office ◽

United States Patent ◽

Planning Tool ◽

Virtual Libraries ◽

Synthesis Planning ◽

Synthetic Routes ◽

The Cost ◽

States Patent

Computer Assisted Synthesis Planning (CASP) has gained considerable interest as of late. Herein we investigate a template-based retrosynthetic planning tool, trained on a variety of datasets consisting of up to 17.5 million reactions. We demonstrate that models trained on datasets such as internal Electronic Laboratory Notebooks (ELN), and the publicly available United States Patent Office (USPTO) extracts, are sufficient for the prediction of full synthetic routes to compounds of interest in medicinal chemistry. As such we have assessed the models on 1,731 compounds from 41 virtual libraries for which experimental results were known. Furthermore, we show that accuracy is a misleading metric for assessment of the ‘filter network’, and propose that the number of successfully applied templates, in conjunction with the overall ability to generate full synthetic routes be examined instead. To this end we found that the specificity of the templates comes at the cost of generalizability, and overall model performance. This is supplemented by a comparison of the underlying datasets and their corresponding models.

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A graph-based genetic algorithm and generative model/Monte Carlo tree search for the exploration of chemical space

Chemical Science ◽

10.1039/c8sc05372c ◽

2019 ◽

Vol 10 (12) ◽

pp. 3567-3572 ◽

Cited By ~ 24

Author(s):

Jan H. Jensen

Keyword(s):

Machine Learning ◽

Genetic Algorithm ◽

Monte Carlo ◽

Chemical Space ◽

Generative Model ◽

Tree Search ◽

Monte Carlo Tree Search ◽

Synthetic Accessibility ◽

Better Than

This paper presents a comparison of a graph-based genetic algorithm (GB-GA) and machine learning (ML) results for the optimization of log P values with a constraint for synthetic accessibility and shows that the GA is as good as or better than the ML approaches for this particular property.

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How Size Matters: Designing Diverse Fragment Libraries for Novel Drug Discovery

Proceedings ◽

10.3390/proceedings2019022107 ◽

2019 ◽

Vol 22 (1) ◽

pp. 107

Author(s):

Yun Shi ◽

Mark von Itzstein

Keyword(s):

Drug Discovery ◽

Chemical Space ◽

Structural Diversity ◽

Optimal Size ◽

Zinc Database ◽

Fragment Library ◽

Size Diversity ◽

Efficient Exploration ◽

Novel Drug ◽

Fragment Libraries

Fragment-based drug discovery (FBDD) has become a major strategy to derive novel lead candidates for both new and established therapeutic targets, as it promises efficient exploration of chemical space by employing fragment-sized (MW 300) compounds. One of the first challenges in implementing a FBDD approach is the design of a fragment library, and more specifically, the choice of its size and individual members. In order to construct a library that maximises the chances of discovering novel chemical matter, a large number of fragments with sufficient structural diversity are often sought. However, the exact diversity of a certain collection of fragments remains elusive, which hinders direct comparisons among different selections of fragments. Building upon structural fingerprints that are commonly utilised in cheminformatics, we herein introduced quantitative measures for the structural diversity of fragment libraries. Structures of commercially available fragments were retrieved from the ZINC database and filtered by physicochemical properties, after which they were subject to selections with library sizes ranging from 100 to 100,000 compounds. The selected libraries were evaluated and compared quantitatively, resulting in interesting size-diversity relationships. Our results suggested the existence of an optimal size for structural diversity and demonstrated that such quantitative measures can guide the design of diverse fragment libraries under various circumstances

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