Articles

DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

In silico prediction of drug–target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. How...

Authors: Maha A. Thafar, Rawan S. Olayan, Haitham Ashoor, Somayah Albaradei, Vladimir B. Bajic, Xin Gao, Takashi Gojobori and Magbubah Essack

One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome

Molecular fingerprints are essential cheminformatics tools for virtual screening and mapping chemical space. Among the different types of fingerprints, substructure fingerprints perform best for small molecule...

Authors: Alice Capecchi, Daniel Probst and Jean-Louis Reymond

Chemical space exploration based on recurrent neural networks: applications in discovering kinase inhibitors

With the rise of artificial intelligence (AI) in drug discovery, de novo molecular generation provides new ways to explore chemical space. However, because de novo molecular generation methods rely on abundant...

Authors: Xuanyi Li, Yinqiu Xu, Hequan Yao and Kejiang Lin

QSAR-derived affinity fingerprints (part 2): modeling performance for potency prediction

Affinity fingerprints report the activity of small molecules across a set of assays, and thus permit to gather information about the bioactivities of structurally dissimilar compounds, where models based on ch...

Authors: Isidro Cortés-Ciriano, Ctibor Škuta, Andreas Bender and Daniel Svozil

The ChemicalToolbox: reproducible, user-friendly cheminformatics analysis on the Galaxy platform

Here, we introduce the ChemicalToolbox, a publicly available web server for performing cheminformatics analysis. The ChemicalToolbox provides an intuitive, graphical interface for common tools for downloading,...

Authors: Simon A. Bray, Xavier Lucas, Anup Kumar and Björn A. Grüning

QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping

An affinity fingerprint is the vector consisting of compound’s affinity or potency against the reference panel of protein targets. Here, we present the QAFFP fingerprint, 440 elements long in silico QSAR-based...

Authors: C. Škuta, I. Cortés-Ciriano, W. Dehaen, P. Kříž, G. J. P. van Westen, I. V. Tetko, A. Bender and D. Svozil

SMILES-based deep generative scaffold decorator for de-novo drug design

Molecular generative models trained with small sets of molecules represented as SMILES strings can generate large regions of the chemical space. Unfortunately, due to the sequential nature of SMILES strings, t...

Authors: Josep Arús-Pous, Atanas Patronov, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen and Ola Engkvist

EDock: blind protein–ligand docking by replica-exchange monte carlo simulation

Protein–ligand docking is an important approach for virtual screening and protein function annotation. Although many docking methods have been developed, most require a high-resolution crystal structure of the...

Authors: Wenyi Zhang, Eric W. Bell, Minghao Yin and Yang Zhang

Assessing the information content of structural and protein–ligand interaction representations for the classification of kinase inhibitor binding modes via machine learning and active learning

For kinase inhibitors, X-ray crystallography has revealed different types of binding modes. Currently, more than 2000 kinase inhibitors with known binding modes are available, which makes it possible to derive...

Authors: Raquel Rodríguez-Pérez, Filip Miljković and Jürgen Bajorath

SYBA: Bayesian estimation of synthetic accessibility of organic compounds

SYBA (SYnthetic Bayesian Accessibility) is a fragment-based method for the rapid classification of organic compounds as easy- (ES) or hard-to-synthesize (HS). It is based on a Bernoulli naïve Bayes classifier ...

Authors: Milan Voršilák, Michal Kolář, Ivan Čmelo and Daniel Svozil

Activity landscape image analysis using convolutional neural networks

Activity landscapes (ALs) are graphical representations that combine compound similarity and activity data. ALs are constructed for visualizing local and global structure–activity relationships (SARs) containe...

Authors: Javed Iqbal, Martin Vogt and Jürgen Bajorath

Quantitative prediction of selectivity between the A₁ and A_2A adenosine receptors

The development of drugs is often hampered due to off-target interactions leading to adverse effects. Therefore, computational methods to assess the selectivity of ligands are of high interest. Currently, sele...

Authors: Lindsey Burggraaff, Herman W. T. van Vlijmen, Adriaan P. IJzerman and Gerard J. P. van Westen

kGCN: a graph-based deep learning framework for chemical structures

Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of...

Authors: Ryosuke Kojima, Shoichi Ishida, Masateru Ohta, Hiroaki Iwata, Teruki Honma and Yasushi Okuno

The diatomic molecular spectroscopy database

The spectroscopy of diatomic molecules is an important research area in chemical physics due to its relevance in astrochemistry, combustion chemistry, and ultracold physics. However, there is currently no data...

Authors: Xiangyue Liu, Stefan Truppe, Gerard Meijer and Jesús Pérez-Ríos

Aromatic clusters in protein–protein and protein–drug complexes

Aromatic rings are important residues for biological interactions and appear to a large extent as part of protein–drug and protein–protein interactions. They are relevant for both protein stability and molecul...

Authors: Esteban Lanzarotti, Lucas A. Defelipe, Marcelo A. Marti and Adrián G. Turjanski

MFsim—an open Java all-in-one rich-client simulation environment for mesoscopic simulation

MFsim is an open Java all-in-one rich-client computing environment for mesoscopic simulation with Jdpd as its default simulation kernel for Molecular Fragment (Dissipative Particle) Dynamics. The new environme...

Authors: Karina van den Broek, Mirco Daniel, Matthias Epple, Jan-Mathis Hein, Hubert Kuhn, Stefan Neumann, Andreas Truszkowski and Achim Zielesny

CReM: chemically reasonable mutations framework for structure generation

Structure generators are widely used in de novo design studies and their performance substantially influences an outcome. Approaches based on the deep learning models and conventional atom-based approaches may...

Authors: Pavel Polishchuk

Inductive transfer learning for molecular activity prediction: Next-Gen QSAR Models with MolPMoFiT

Deep neural networks can directly learn from chemical structures without extensive, user-driven selection of descriptors in order to predict molecular properties/activities with high reliability. But these app...

Authors: Xinhao Li and Denis Fourches

Industry-scale application and evaluation of deep learning for drug target prediction

Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. ...

Authors: Noé Sturm, Andreas Mayr, Thanh Le Van, Vladimir Chupakhin, Hugo Ceulemans, Joerg Wegner, Jose-Felipe Golib-Dzib, Nina Jeliazkova, Yves Vandriessche, Stanislav Böhm, Vojtech Cima, Jan Martinovic, Nigel Greene, Tom Vander Aa, Thomas J. Ashby, Sepp Hochreiter…

AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization

We here present AutoGrow4, an open-source program for semi-automated computer-aided drug discovery. AutoGrow4 uses a genetic algorithm to evolve predicted ligands on demand and so is not limited to a virtual l...

Authors: Jacob O. Spiegel and Jacob D. Durrant

KnowTox: pipeline and case study for confident prediction of potential toxic effects of compounds in early phases of development

Risk assessment of newly synthesised chemicals is a prerequisite for regulatory approval. In this context, in silico methods have great potential to reduce time, cost, and ultimately animal testing as they mak...

Authors: Andrea Morger, Miriam Mathea, Janosch H. Achenbach, Antje Wolf, Roland Buesen, Klaus-Juergen Schleifer, Robert Landsiedel and Andrea Volkamer

SIME: synthetic insight-based macrolide enumerator to generate the V1B library of 1 billion macrolides

We report on a new cheminformatics enumeration technology—SIME, synthetic insight-based macrolide enumerator—a new and improved software technology. SIME can enumerate fully assembled macrolides with synthetic...

Authors: Phyo Phyo Kyaw Zin, Gavin Williams and Denis Fourches

GEN: highly efficient SMILES explorer using autodidactic generative examination networks

Recurrent neural networks have been widely used to generate millions of de novo molecules in defined chemical spaces. Reported deep generative models are exclusively based on LSTM and/or GRU units and frequent...

Authors: Ruud van Deursen, Peter Ertl, Igor V. Tetko and Guillaume Godin

Predicting liver cytosol stability of small molecules

Over the last few decades, chemists have become skilled at designing compounds that avoid cytochrome P (CYP) 450 mediated metabolism. Typical screening assays are performed in liver microsomal fractions and it...

Authors: Pranav Shah, Vishal B. Siramshetty, Alexey V. Zakharov, Noel T. Southall, Xin Xu and Dac-Trung Nguyen

Review on natural products databases: where to find data in 2020

Natural products (NPs) have been the centre of attention of the scientific community in the last decencies and the interest around them continues to grow incessantly. As a consequence, in the last 20 years, th...

Authors: Maria Sorokina and Christoph Steinbeck

Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications

Ensemble learning helps improve machine learning results by combining several models and allows the production of better predictive performance compared to a single model. It also benefits and accelerates the ...

Authors: Chia-Hsiu Chen, Kenichi Tanaka, Masaaki Kotera and Kimito Funatsu

COVER: conformational oversampling as data augmentation for molecules

Training neural networks with small and imbalanced datasets often leads to overfitting and disregard of the minority class. For predictive toxicology, however, models with a good balance between sensitivity an...

Authors: Jennifer Hemmerich, Ece Asilar and Gerhard F. Ecker

Transformer-CNN: Swiss knife for QSAR modeling and interpretation

We present SMILES-embeddings derived from the internal encoder state of a Transformer [1] model trained to canonize SMILES as a Seq2Seq problem. Using a CharNN [2] architecture upon the embeddings results in high...

Authors: Pavel Karpov, Guillaume Godin and Igor V. Tetko

ADMET evaluation in drug discovery. 20. Prediction of breast cancer resistance protein inhibition through machine learning

Breast cancer resistance protein (BCRP/ABCG2), an ATP-binding cassette (ABC) efflux transporter, plays a critical role in multi-drug resistance (MDR) to anti-cancer drugs and drug–drug interactions. The predic...

Authors: Dejun Jiang, Tailong Lei, Zhe Wang, Chao Shen, Dongsheng Cao and Tingjun Hou

A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility

Efficient and accurate prediction of molecular properties, such as lipophilicity and solubility, is highly desirable for rational compound design in chemical and pharmaceutical industries. To this end, we buil...

Authors: Bowen Tang, Skyler T. Kramer, Meijuan Fang, Yingkun Qiu, Zhen Wu and Dong Xu

Multiobjective de novo drug design with recurrent neural networks and nondominated sorting

Research productivity in the pharmaceutical industry has declined significantly in recent decades, with higher costs, longer timelines, and lower success rates of drug candidates in clinical trials. This has p...

Authors: Jacob Yasonik

Correction to: Interoperable chemical structure search service

It was highlighted that the original article [1] contained an error in the last paragraph of the section ‘Structure search using SPARQL’, specifically in the radius of the used fingerprint. This Correction art...

Authors: Miroslav Kratochvíl, Jiří Vondrášek and Jakub Galgonek

Visualization of very large high-dimensional data sets as minimum spanning trees

The chemical sciences are producing an unprecedented amount of large, high-dimensional data sets containing chemical structures and associated properties. However, there are currently no algorithms to visualiz...

Authors: Daniel Probst and Jean-Louis Reymond

Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity

Chemogenomics, also called proteochemometrics, covers a range of computational methods that can be used to predict protein–ligand interactions at large scales in the protein and chemical spaces. They differ fr...

Authors: Benoit Playe and Veronique Stoven

The C++ programming language in cheminformatics and computational chemistry

This paper describes salient features of the C++ programming language and its programming ecosystem, with emphasis on how the language affects scientific software development. Brief history of C++ and its pred...

Authors: Dmitrii Rassokhin

Towards reproducible computational drug discovery

The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilizat...

Authors: Nalini Schaduangrat, Samuel Lampa, Saw Simeon, Matthew Paul Gleeson, Ola Spjuth and Chanin Nantasenamat

Craig plot 2.0: an interactive navigation in the substituent bioisosteric space

Bioisosteric replacement is a standard technique that is used in medicinal chemistry to design analogs of bioactive molecules with similar biological activity and with additional improved characteristics. Succ...

Authors: Peter Ertl

3DStructGen: an interactive web-based 3D structure generation for non-periodic molecule and crystal

The increasing number of organic and inorganic structures promotes the development of the “Big Data” in chemistry and material science, and raises the need for cross-platform and web-based methods to search, v...

Authors: Pin Chen, Yu Wang, Hui Yan, Sen Gao, Zexin Xu, Yangzhong Li, Qing Mo, Junkang Huang, Jun Tao, GeChuanqi Pan, Jiahui Li and Yunfei Du

Development of Natural Compound Molecular Fingerprint (NC-MFP) with the Dictionary of Natural Products (DNP) for natural product-based drug development

Computer-aided research on the relationship between molecular structures of natural compounds (NC) and their biological activities have been carried out extensively because the molecular structures of new drug...

Authors: Myungwon Seo, Hyun Kil Shin, Yoochan Myung, Sungbo Hwang and Kyoung Tai No

SmartGraph: a network pharmacology investigation platform

Drug discovery investigations need to incorporate network pharmacology concepts while navigating the complex landscape of drug-target and target-target interactions. This task requires solutions that integrate...

Authors: Gergely Zahoránszky-Kőhalmi, Timothy Sheils and Tudor I. Oprea

Learning cheminformatics

Authors: Rajarshi Guha and Egon Willighagen

The rcdk and cluster R packages applied to drug candidate selection

The aim of this article is to show how thevpower of statistics and cheminformatics can be combined, in R, using two packages: rcdk and cluster.

Authors: Adrian Voicu, Narcis Duteanu, Mirela Voicu, Daliborca Vlad and Victor Dumitrascu

Mol-CycleGAN: a generative model for molecular optimization

Designing a molecule with desired properties is one of the biggest challenges in drug development, as it requires optimization of chemical compound structures with respect to many complex properties. To improv...

Authors: Łukasz Maziarka, Agnieszka Pocha, Jan Kaczmarczyk, Krzysztof Rataj, Tomasz Danel and Michał Warchoł

Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction

Neural Message Passing for graphs is a promising and relatively recent approach for applying Machine Learning to networked data. As molecules can be described intrinsically as a molecular graph, it makes sense...

Authors: M. Withnall, E. Lindelöf, O. Engkvist and H. Chen

CyBy²: a strongly typed, purely functional framework for chemical data management

We present the development of CyBy², a versatile framework for chemical data management written in purely functional style in Scala, a modern multi-paradigm programming language. Together with the core libraries ...

Authors: Stefan Höck and Rainer Riedl

Learning important features from multi-view data to predict drug side effects

The problem of drug side effects is one of the most crucial issues in pharmacological development. As there are many limitations in current experimental and clinical methods for detecting side effects, a lot o...

Authors: Xujun Liang, Pengfei Zhang, Jun Li, Ying Fu, Lingzhi Qu, Yongheng Chen and Zhuchu Chen

IntelliPatent: a web-based intelligent system for fast chemical patent claim drafting

The first step of automating composition patent drafting is to draft the claims around a Markush structure with substituents. Currently, this process depends heavily on experienced attorneys or patent agents, ...

Authors: Pei-Hua Wang and Yufeng Jane Tseng

ChemScanner: extraction and re-use(ability) of chemical information from common scientific documents containing ChemDraw files

We developed ChemScanner, a software that can be used for the extraction of chemical information from ChemDraw binary (CDX) or ChemDraw XML-based (CDXML) files and to retrieve the ChemDraw scheme from DOC, DOCX o...

Authors: An Nguyen, Yu-Chieh Huang, Pierre Tremouilhac, Nicole Jung and Stefan Bräse

The chemfp project

The chemfp project has had four main goals: (1) promote the FPS format as a text-based exchange format for dense binary cheminformatics fingerprints, (2) develop a high-performance implementation of the BitBou...

Authors: Andrew Dalke

HastaLaVista, a web-based user interface for NMR-based untargeted metabolic profiling analysis in biomedical sciences: towards a new publication standard

Metabolic profiling has been shown to be useful to improve our understanding of complex metabolic processes. Shared data are key to the analysis and validation of metabolic profiling and untargeted spectral an...

Authors: Julien Wist