scholarly journals Diketomorpholines: Synthetic Accessibility and Utilization

ACS Omega ◽  
2021 ◽  
Author(s):  
Lan Phuong Vu ◽  
Michael Gütschow
Keyword(s):  
Synthetic Accessibility

Medicinal Chemistry Database GDBMedChem

2019 ◽  
Author(s):  
Mahendra Awale ◽  
Finton Sirockin ◽  
Nikolaus Stiefl ◽  
Jean-Louis Reymond
Keyword(s):  
Small Molecules ◽  
Natural Product ◽  
Medicinal Chemistry ◽  
3D Visualization ◽  
Molecular Size ◽  
Similarity Searching ◽  
Complex Molecules ◽  
Synthetic Accessibility ◽  
Simple Chemical ◽  
Reduced Complexity

<div>The generated database GDB17 enumerates 166.4 billion possible molecules up to 17 atoms of C, N, O, S and halogens following simple chemical stability and synthetic feasibility rules, however medicinal chemistry criteria are not taken into account. Here we applied rules inspired by medicinal chemistry to exclude problematic functional groups and complex molecules from GDB17, and sampled the resulting subset evenly across molecular size, stereochemistry and polarity to form GDBMedChem as a compact collection of 10 million small molecules.</div><div><br></div><div>This collection has reduced complexity and better synthetic accessibility than the entire GDB17 but retains higher sp 3 - carbon fraction and natural product likeness scores compared to known drugs. GDBMedChem molecules are more diverse and very different from known molecules in terms of substructures and represent an unprecedented source of diversity for drug design. GDBMedChem is available for 3D-visualization, similarity searching and for download at http://gdb.unibe.ch.</div>

Structural study of anhydrous and hydrated 5-fluorouracil co-crystals with nicotinamide and isonicotinamide

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Tobias Heinen ◽  
Sandra Hoelscher ◽  
Vera Vasylyeva
Keyword(s):  
Structural Study ◽  
Solvent Free ◽  
Cancer Drug ◽  
Pharmaceutical Applications ◽  
Synthetic Accessibility ◽  
Anti Cancer ◽  
Component Systems ◽  
The Given

Abstract 5-Fluorouracil is a widely used anti-cancer drug which exhibits diverse polymorphic and co-crystalline behavior. Here we report two new solvent-free co-crystals of 5-fluorouracil with model co-formers nicotinamide and isonicotinamide, along with the redetermination of their hydrated analogues. Selected co-formers are categorized as safe and therefore suitable for pharmaceutical applications. Differences and similarities in supramolecular topology of the given structures are discussed. A special emphasis is set on the influence of fluorine moieties on the overall packing and synthetic accessibility of the presented multi-component systems.

scholarly journals Materials Precursor Score: Modelling Chemists' Intuition for the Synthetic Accessibility of Porous Organic Cages

2021 ◽  
Author(s):  
Steven Bennett ◽  
Filip Szczypiński ◽  
Lukas Turcani ◽  
Michael Briggs ◽  
Rebecca L. Greenaway ◽  
...  
Keyword(s):  
Organic Molecules ◽  
Class Imbalance ◽  
Computational Prediction ◽  
Material Development ◽  
Computational Screening ◽  
Machine Learning Model ◽  
Synthetic Accessibility ◽  
Internal Cavities ◽  
Computational Discovery ◽  
Novel Structures

<div>Computation is increasingly being used to try to accelerate the discovery of new materials. One specific example of this is porous molecular materials, specifically porous organic cages, where the porosity of the materials predominantly comes from the internal cavities of the molecules themselves. The computational discovery of novel structures with useful properties is currently hindered by the difficulty in transitioning from a computational prediction to synthetic realisation. Attempts at experimental validation are often time-consuming, expensive and, frequently, the key bottleneck of material discovery. In this work, we developed a computational screening workflow for porous molecules that includes consideration of the synthetic difficulty of material precursors, aimed at easing the transition between computational prediction and experimental realisation. We trained a machine learning model by first collecting data on 12,553 molecules categorised either as `easy-to-synthesise' or `difficult-to-synthesise' by expert chemists with years of experience in organic synthesis. We used an approach to address the class imbalance present in our dataset, producing a binary classifier able to categorise easy-to-synthesise molecules with few false positives. We then used our model during computational screening for porous organic molecules to bias towards precursors whose easier synthesis requirements would make them promising candidates for experimental realisation and material development. We found that even by limiting precursors to those that are easier-to-synthesise, we are still able to identify cages with favourable, and even some rare, properties. </div>

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

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

<p>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. </p>

scholarly journals Multicriterial analysis of the accessibility of public transport stops in Cracow

2020 ◽  
Vol 23 (4) ◽  
pp. 32-41
Author(s):  
Martin Bárta ◽  
Tomas Masopust
Keyword(s):  
Public Transport ◽  
Multicriteria Analysis ◽  
Buffer Zone ◽  
Research Area ◽  
Transport Accessibility ◽  
Synthetic Accessibility ◽  
Comprehensive Picture ◽  
The Mean ◽  
The Individual ◽  
The City

This study deals with the synthesis of selected attributes of public transport accessibility. The aim is to present a new method of multi-criteria analysis. As the research area, the city of Cracow has been chosen. The GTFS (General Transit Feed Specification) system has been used to obtain traffic data for buses and trams within the city‘s transport company (MPK Krakow). The analysis itself consists of 4 main accessibility indicators (walking time to each stop, number of lines, directions, and connections from each stop). The problem of exceeding the stops accessibility beyond the administrative border of Cracow has been solved by using a 500 m wide buffer zone around the city. To connect the individual layers of indicators into a multicriteria analysis, the Voronoi diagram function has been applied. The results of the method are presented in the form of synthetic maps of transport accessibility for each bus and tram stop in Cracow. Together with the synthetic accessibility maps, an index of a stop importance has been created as well, which consists of the sum of the mean percentages from 3 indicators (number of lines, directions, connections). The synthetic method used and acquired detailed values not only for the city of Cracow as a whole, but also its individual parts make it possible to provide a comprehensive picture of accessibility by public transport. This multicriteria analysis can also be extended for a comparative study of selected cities.

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

2019 ◽  
Vol 10 (12) ◽  
pp. 3567-3572 ◽  
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.

Wisdom of crowds for synthetic accessibility evaluation

2018 ◽  
Vol 80 ◽  
pp. 217-223 ◽  
Author(s):  
Yukino Baba ◽  
Tetsu Isomura ◽  
Hisashi Kashima
Keyword(s):  
Wisdom Of Crowds ◽  
Synthetic Accessibility ◽  
Accessibility Evaluation

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

2019 ◽  
Author(s):  
Amol Thakkar ◽  
Nidhal Selmi ◽  
Jean-Louis Reymond ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Chemical Space ◽  
Ring System ◽  
Ring Systems ◽  
Multiple Reactions ◽  
Zinc Database ◽  
Synthetic Accessibility ◽  
Synthesis Planning ◽  
Reaction Graphs ◽  
Synthetic Routes ◽  
The Relationship

<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>

scholarly journals Prediction of Compound Synthesis Accessibility Based on Reaction Knowledge Graph

2021 ◽  
Author(s):  
Baiqing Li ◽  
Hongming Chen
Keyword(s):  
Deep Learning ◽  
Prediction Models ◽  
De Novo ◽  
Quantitative Estimation ◽  
Reaction Network ◽  
Generative Models ◽  
Machine Learning Algorithms ◽  
Scoring Functions ◽  
Synthetic Accessibility ◽  
Learning Machine

<a>With the increasing application of deep learning based generative models for <i>de novo</i> molecule design, quantitative estimation of molecular synthetic accessibility becomes a crucial factor for prioritizing the structures generated from generative models. On the other hand, it is also useful for helping prioritization of hit/lead compounds and guiding retro-synthesis analysis. In current study, based on the USPTO and Pistachio reaction datasets, we created a chemical reaction network, in which a depth-first search was performed for identification of the reaction paths of product compounds. This reaction dataset was then used to build predictive model for distinguishing the organic compounds either as easy synthesize (ES) or hard-to synthesize (HS) classes. Three synthesis accessibility (SA) models were built using deep learning/machine learning algorithms. The comparison between our three SA scoring functions with other existing synthesis accessibility scoring schemes, such as SYBA, SCScore, SAScore were also carried out. and the graph based deep learning model outperforms those existing SA scores. Our results show that prediction models based on historical reaction knowledge could be a useful tool for measuring molecule complexity and estimating molecule SA.</a>

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