The next level in chemical space navigation: going far beyond enumerable compound libraries

AbstractThe spirocyclic motif is abundant in natural products and provides an ideal three-dimensional template to interact with biological targets. With significant attention historically expended on the synthesis of flat-heterocyclic compound libraries, methods to access the less-explored three-dimensional medicinal-chemical space will continue to increase in demand. Herein, we highlight by reaction class the common strategies used to construct the spirocyclic centres embedded in a series of well-studied natural products.1 Introduction2 Cycloadditions3 Palladium-Catalysed Coupling Reactions4 Conjugate Additions5 Imines, Aminals, and Hemiaminal Ethers6 Mannich-Type Reactions7 Oxidative Dearomatisation8 Alkylation9 Organometallic Additions10 Conclusions

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Chemoinformatics-based enumeration of chemical libraries: a tutorial

Journal of Cheminformatics ◽

10.1186/s13321-020-00466-z ◽

2020 ◽

Vol 12 (1) ◽

Cited By ~ 1

Author(s):

Fernanda I. Saldívar-González ◽

C. Sebastian Huerta-García ◽

José L. Medina-Franco

Keyword(s):

Teaching And Learning ◽

Chemical Space ◽

Acetylcholinesterase Inhibitors ◽

Computer Assisted ◽

Diversity Oriented Synthesis ◽

Chemical Libraries ◽

Chemical Reagents ◽

Compound Libraries ◽

Oriented Molecules ◽

Step Procedure

Abstract Virtual compound libraries are increasingly being used in computer-assisted drug discovery applications and have led to numerous successful cases. This paper aims to examine the fundamental concepts of library design and describe how to enumerate virtual libraries using open source tools. To exemplify the enumeration of chemical libraries, we emphasize the use of pre-validated or reported reactions and accessible chemical reagents. This tutorial shows a step-by-step procedure for anyone interested in designing and building chemical libraries with or without chemoinformatics experience. The aim is to explore various methodologies proposed by synthetic organic chemists and explore affordable chemical space using open-access chemoinformatics tools. As part of the tutorial, we discuss three examples of design: a Diversity-Oriented-Synthesis library based on lactams, a bis-heterocyclic combinatorial library, and a set of target-oriented molecules: isoindolinone based compounds as potential acetylcholinesterase inhibitors. This manuscript also seeks to contribute to the critical task of teaching and learning chemoinformatics.

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Compound property enhancement by virtual compound synthesis

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720018400164 ◽

2018 ◽

Vol 16 (03) ◽

pp. 1840016 ◽

Cited By ~ 4

Author(s):

Naoki Arai ◽

Shunsuke Yoshikawa ◽

Nobuaki Yasuo ◽

Ryunosuke Yoshino ◽

Masakazu Sekijima

Keyword(s):

Chemical Space ◽

Computational Cost ◽

Pharmaceutical Products ◽

Drug Candidates ◽

Compound Libraries ◽

Custom Made ◽

Compound Synthesis ◽

Synthetic Reaction ◽

Structural Preservation ◽

Synthesis Reactions

During drug discovery, drug candidates are narrowed down over several steps to develop pharmaceutical products. The theoretical chemical space in such steps is estimated to be [Formula: see text]. To cover that space, extensive virtual compound libraries have been developed; however, the compilation of extensive libraries comes at large computational cost. Thus, to reduce the computational cost, researchers have constructed custom-made virtual compound libraries that focus on target diseases. In this study, we develop a system that generates virtual compound libraries from input compounds. When a user inputs a compound, the system recursively applies virtual synthetic reaction rules to the compound to improve its properties. The synthetic pathway can also be traced by the user because the reaction rules in this system are based on real organic synthesis reactions. This system has useful functions for effective drug design, such as structural preservation, allowing the substructures necessary for potency to be maintained. In this paper, to confirm the effect of directional reaction sets, we applied the reaction sets to 100 compounds. Moreover, to confirm that the system can reproduce real synthetic pathways, the synthetic pathways of Ibuprofen and Ofloxacin were explored by inputting isobutyl benzene and 7,8-difluoro-2,3-dihydro-3-methyl-4H-benzoxazine. This application is available at the following URL: http://enh.sekijima-lab.org .

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Photo-mediated ring contraction of saturated heterocycles

Science ◽

10.1126/science.abi7183 ◽

2021 ◽

pp. eabi7183

Author(s):

Justin Jurczyk ◽

Michaelyn C. Lux ◽

Donovon Adpressa ◽

Sojung F. Kim ◽

Yu-hong Lam ◽

...

Keyword(s):

Chemical Space ◽

Type Ii ◽

Specific Chemical ◽

Ring Contraction ◽

Bioactive Natural Products ◽

Compound Libraries ◽

Norrish Type ◽

And Function ◽

Core Framework ◽

Saturated Heterocycles

Saturated heterocycles are found in numerous therapeutics as well as bioactive natural products and are abundant in many medicinal and agrochemical compound libraries. To access new chemical space and function, many methods for functionalization on the periphery of these structures have been developed. Comparatively fewer methods are known for restructuring their core framework. Herein, we describe a visible light-mediated ring contraction of α-acylated saturated heterocycles. This unconventional transformation is orthogonal to traditional ring contractions, challenging the paradigm for diversification of heterocycles including piperidine, morpholine, thiane, tetrahydropyran, and tetrahydroisoquinoline derivatives. The success of this Norrish Type II variant rests on reactivity differences between photoreactive ketone groups in specific chemical environments. This strategy was applied to late-stage remodeling of pharmaceutical derivatives, peptides, and sugars.

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A blueprint for high affinity SARS-CoV-2 Mpro inhibitors from activity-based compound library screening guided by analysis of protein dynamics

10.1101/2020.12.14.422634 ◽

2020 ◽

Author(s):

Jonas Gossen ◽

Simone Albani ◽

Anton Hanke ◽

Benjamin P. Joseph ◽

Cathrine Bergh ◽

...

Keyword(s):

Ligand Binding ◽

Binding Site ◽

Active Sites ◽

Chemical Space ◽

Protein Structures ◽

Rational Drug Design ◽

In Vitro Assays ◽

Strong Basis ◽

High Affinity ◽

Compound Libraries

AbstractThe SARS-CoV-2 coronavirus outbreak continues to spread at a rapid rate worldwide. The main protease (Mpro) is an attractive target for anti-COVID-19 agents. Unfortunately, unexpected difficulties have been encountered in the design of specific inhibitors. Here, by analyzing an ensemble of ~30,000 SARS-CoV-2 Mpro conformations from crystallographic studies and molecular simulations, we show that small structural variations in the binding site dramatically impact ligand binding properties. Hence, traditional druggability indices fail to adequately discriminate between highly and poorly druggable conformations of the binding site. By performing ~200 virtual screenings of compound libraries on selected protein structures, we redefine the protein’s druggability as the consensus chemical space arising from the multiple conformations of the binding site formed upon ligand binding. This procedure revealed a unique SARS-CoV-2 Mpro blueprint that led to a definition of a specific structure-based pharmacophore. The latter explains the poor transferability of potent SARS-CoV Mpro inhibitors to SARS-CoV-2 Mpro, despite the identical sequences of the active sites. Importantly, application of the pharmacophore predicted novel high affinity inhibitors of SARS-CoV-2 Mpro, that were validated by in vitro assays performed here and by a newly solved X-ray crystal structure. These results provide a strong basis for effective rational drug design campaigns against SARS-CoV-2 Mpro and a new computational approach to screen protein targets with malleable binding sites.

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Improving Chemical Autoencoder Latent Space and Molecular De Novo Generation Diversity with Heteroencoders

Biomolecules ◽

10.3390/biom8040131 ◽

2018 ◽

Vol 8 (4) ◽

pp. 131 ◽

Cited By ~ 30

Author(s):

Esben Bjerrum ◽

Boris Sattarov

Keyword(s):

Short Term Memory ◽

De Novo ◽

Chemical Space ◽

Quantitative Structure Activity Relationship ◽

Network Architectures ◽

Space Navigation ◽

Latent Space ◽

Circular Fingerprints ◽

Areas Of Interest ◽

Space Distance

Chemical autoencoders are attractive models as they combine chemical space navigation with possibilities for de novo molecule generation in areas of interest. This enables them to produce focused chemical libraries around a single lead compound for employment early in a drug discovery project. Here, it is shown that the choice of chemical representation, such as strings from the simplified molecular-input line-entry system (SMILES), has a large influence on the properties of the latent space. It is further explored to what extent translating between different chemical representations influences the latent space similarity to the SMILES strings or circular fingerprints. By employing SMILES enumeration for either the encoder or decoder, it is found that the decoder has the largest influence on the properties of the latent space. Training a sequence to sequence heteroencoder based on recurrent neural networks (RNNs) with long short-term memory cells (LSTM) to predict different enumerated SMILES strings from the same canonical SMILES string gives the largest similarity between latent space distance and molecular similarity measured as circular fingerprints similarity. Using the output from the code layer in quantitative structure activity relationship (QSAR) of five molecular datasets shows that heteroencoder derived vectors markedly outperforms autoencoder derived vectors as well as models built using ECFP4 fingerprints, underlining the increased chemical relevance of the latent space. However, the use of enumeration during training of the decoder leads to a marked increase in the rate of decoding to different molecules than encoded, a tendency that can be counteracted with more complex network architectures.

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SAR by Space: Enriching Hit Sets from the Chemical Space

Molecules ◽

10.3390/molecules24173096 ◽

2019 ◽

Vol 24 (17) ◽

pp. 3096 ◽

Cited By ~ 4

Author(s):

Franca-Maria Klingler ◽

Marcus Gastreich ◽

Oleksandr Grygorenko ◽

Olena Savych ◽

Petro Borysko ◽

...

Keyword(s):

Chemical Space ◽

Scoring Function ◽

Building Blocks ◽

Real Space ◽

Computer Hardware ◽

Space Navigation ◽

Synthetic Accessibility ◽

Ic50 Values ◽

Crucial Part ◽

Selection Of

We introduce SAR by Space, a concept to drastically accelerate structure-activity relationship (SAR) elucidation by synthesizing neighboring compounds that originate from vast chemical spaces. The space navigation is accomplished within minutes on affordable standard computer hardware using a tree-based molecule descriptor and dynamic programming. Maximizing the synthetic accessibility of the results from the computer is achieved by applying a careful selection of building blocks in combination with suitably chosen reactions; a decade of in-house quality control shows that this is a crucial part in the process. The REAL Space is the largest chemical space of commercially available compounds, counting 11 billion molecules as of today. It was used to mine actives against bromodomain 4 (BRD4). Before synthesis, compounds were docked into the binding site using a scoring function, which incorporates intrinsic desolvation terms, thus avoiding time-consuming simulations. Five micromolar hits have been identified and verified within less than six weeks, including the measurement of IC50 values. We conclude that this procedure is a substantial time-saver, accelerating both ligand and structure-based approaches in hit generation and lead optimization stages.

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Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Frontiers in Chemistry ◽

10.3389/fchem.2021.662688 ◽

2021 ◽

Vol 9 ◽

Author(s):

Kauê Santana ◽

Lidiane Diniz do Nascimento ◽

Anderson Lima e Lima ◽

Vinícius Damasceno ◽

Claudio Nahum ◽

...

Keyword(s):

Natural Products ◽

Virtual Screening ◽

Bioactive Compounds ◽

Chemical Space ◽

Structural Diversity ◽

Industrial Applications ◽

Bioactive Molecules ◽

Natural Sources ◽

Compound Libraries ◽

Pharmacokinetic Properties

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

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Generating Customized Compound Libraries for Drug Discovery with Machine Intelligence

10.26434/chemrxiv.10119299 ◽

2019 ◽

Author(s):

Michael Moret ◽

Lukas Friedrich ◽

Francesca Grisoni ◽

Daniel Merk ◽

Gisbert Schneider

Keyword(s):

Drug Discovery ◽

De Novo ◽

Chemical Space ◽

Primary Source ◽

Structural Diversity ◽

Machine Intelligence ◽

Data Driven ◽

Learning Framework ◽

Compound Libraries ◽

Chemical Knowledge

<div> <div> <p>Generative machine learning models sample drug-like molecules from chemical space without the need for explicit design rules. A deep learning framework for customized compound library generation is presented, aiming to enrich and expand the pharmacologically relevant chemical space with new molecular entities ‘on demand’. This de novo design approach was used to generate molecules that combine features from bioactive synthetic compounds and natural products, which are a primary source of inspiration for drug discovery. The results show that the data-driven machine intelligence acquires implicit chemical knowledge and generates novel molecules with bespoke properties and structural diversity. The method is available as an open-access tool for medicinal and bioorganic chemistry.<br></p> </div> </div>

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Generating Customized Compound Libraries for Drug Discovery with Machine Intelligence

10.26434/chemrxiv.10119299.v1 ◽

2019 ◽

Author(s):

Michael Moret ◽

Lukas Friedrich ◽

Francesca Grisoni ◽

Daniel Merk ◽

Gisbert Schneider

Keyword(s):

Drug Discovery ◽

De Novo ◽

Chemical Space ◽

Primary Source ◽

Structural Diversity ◽

Machine Intelligence ◽

Data Driven ◽

Learning Framework ◽

Compound Libraries ◽

Chemical Knowledge

<div> <div> <p>Generative machine learning models sample drug-like molecules from chemical space without the need for explicit design rules. A deep learning framework for customized compound library generation is presented, aiming to enrich and expand the pharmacologically relevant chemical space with new molecular entities ‘on demand’. This de novo design approach was used to generate molecules that combine features from bioactive synthetic compounds and natural products, which are a primary source of inspiration for drug discovery. The results show that the data-driven machine intelligence acquires implicit chemical knowledge and generates novel molecules with bespoke properties and structural diversity. The method is available as an open-access tool for medicinal and bioorganic chemistry.<br></p> </div> </div>

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