Articles

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

A de novo molecular generation method using latent vector based generative adversarial network

Deep learning methods applied to drug discovery have been used to generate novel structures. In this study, we propose a new deep learning architecture, LatentGAN, which combines an autoencoder and a generativ...

Authors: Oleksii Prykhodko, Simon Viet Johansson, Panagiotis-Christos Kotsias, Josep Arús-Pous, Esben Jannik Bjerrum, Ola Engkvist and Hongming Chen

ACID: a free tool for drug repurposing using consensus inverse docking strategy

Drug repurposing offers a promising alternative to dramatically shorten the process of traditional de novo development of a drug. These efforts leverage the fact that a single molecule can act on multiple targ...

Authors: Fan Wang, Feng-Xu Wu, Cheng-Zhang Li, Chen-Yang Jia, Sun-Wen Su, Ge-Fei Hao and Guang-Fu Yang

A comprehensive analysis of the history of DFT based on the bibliometric method RPYS

This bibliometric study aims at providing a comprehensive analysis of the history of density functional theory (DFT) from a perspective of chemistry by using reference publication year spectroscopy (RPYS). 114...

Authors: Robin Haunschild, Andreas Barth and Bernie French

Randomized SMILES strings improve the quality of molecular generative models

Recurrent Neural Networks (RNNs) trained with a set of molecules represented as unique (canonical) SMILES strings, have shown the capacity to create large chemical spaces of valid and meaningful structures. He...

Authors: Josep Arús-Pous, Simon Viet Johansson, Oleksii Prykhodko, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen and Ola Engkvist

Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation

With the advancements in deep learning, deep generative models combined with graph neural networks have been successfully employed for data-driven molecular graph generation. Early methods based on the non-aut...

Authors: Youngchun Kwon, Jiho Yoo, Youn-Suk Choi, Won-Joon Son, Dongseon Lee and Seokho Kang

Dataset’s chemical diversity limits the generalizability of machine learning predictions

The QM9 dataset has become the golden standard for Machine Learning (ML) predictions of various chemical properties. QM9 is based on the GDB, which is a combinatorial exploration of the chemical space. ML mole...

Authors: Marta Glavatskikh, Jules Leguy, Gilles Hunault, Thomas Cauchy and Benoit Da Mota

Multi-task learning with a natural metric for quantitative structure activity relationship learning

The goal of quantitative structure activity relationship (QSAR) learning is to learn a function that, given the structure of a small molecule (a potential drug), outputs the predicted activity of the compound....

Authors: Noureddin Sadawi, Ivan Olier, Joaquin Vanschoren, Jan N. van Rijn, Jeremy Besnard, Richard Bickerton, Crina Grosan, Larisa Soldatova and Ross D. King

Identifying new topoisomerase II poison scaffolds by combining publicly available toxicity data and 2D/3D-based virtual screening

Molecular descriptor (2D) and three dimensional (3D) shape based similarity methods are widely used in ligand based virtual drug design. In the present study pairwise structure comparisons among a set of 4858 ...

Authors: Anna Lovrics, Veronika F. S. Pape, Dániel Szisz, Adrián Kalászi, Petra Heffeter, Csaba Magyar and Gergely Szakács

A multiple classifier system identifies novel cannabinoid CB2 receptor ligands

Drugs have become an essential part of our lives due to their ability to improve people’s health and quality of life. However, for many diseases, approved drugs are not yet available or existing drugs have und...

Authors: David Ruano-Ordás, Lindsey Burggraaff, Rongfang Liu, Cas van der Horst, Laura H. Heitman, Michael T. M. Emmerich, Jose R. Mendez, Iryna Yevseyeva and Gerard J. P. van Westen

Missed opportunities in large scale comparison of QSAR and conformal prediction methods and their applications in drug discovery

Recently Bosc et al. (J Cheminform 11(1): 4, 2019), published an article describing a case study that directly compares conformal predictions with traditional QSAR methods for large-scale predictions of target...

Authors: Damjan Krstajic

Reply to “Missed opportunities in large scale comparison of QSAR and conformal prediction methods and their applications in drug discovery”

In response to Krstajic’s letter to the editor concerning our published paper, we here take the opportunity to reply, to re-iterate that no errors in our work were identified, to provide further details, and t...

Authors: Nicolas Bosc, Francis Atkinson, Eloy Félix, Anna Gaulton, Anne Hersey and Andrew R. Leach

A visual approach for analysis and inference of molecular activity spaces

Molecular space visualization can help to explore the diversity of large heterogeneous chemical data, which ultimately may increase the understanding of structure-activity relationships (SAR) in drug discovery...

Authors: Samina Kausar and Andre O. Falcao

A path to next-generation reproducibility in cheminformatics

Currently, the submission guidelines for the Journal of Cheminformatics say it will “only publish research or software that is entirely reproducible by third parties.” They go on to specify that being reproduc...

Authors: Robert D. Clark

A general approach for retrosynthetic molecular core analysis

Scaffold analysis of compound data sets has reemerged as a chemically interpretable alternative to machine learning for chemical space and structure–activity relationships analysis. In this context, analog ser...

Authors: J. Jesús Naveja, B. Angélica Pilón-Jiménez, Jürgen Bajorath and José L. Medina-Franco

Open-source QSAR models for pKa prediction using multiple machine learning approaches

The logarithmic acid dissociation constant pKa reflects the ionization of a chemical, which affects lipophilicity, solubility, protein binding, and ability to pass through the plasma membrane. Thus, pKa affect...

Authors: Kamel Mansouri, Neal F. Cariello, Alexandru Korotcov, Valery Tkachenko, Chris M. Grulke, Catherine S. Sprankle, David Allen, Warren M. Casey, Nicole C. Kleinstreuer and Antony J. Williams

Machine learning models for hydrogen bond donor and acceptor strengths using large and diverse training data generated by first-principles interaction free energies

We present machine learning (ML) models for hydrogen bond acceptor (HBA) and hydrogen bond donor (HBD) strengths. Quantum chemical (QC) free energies in solution for 1:1 hydrogen-bonded complex formation to th...

Authors: Christoph A. Bauer, Gisbert Schneider and Andreas H. Göller

SAR and QSAR modeling of a large collection of LD₅₀ rat acute oral toxicity data

The median lethal dose for rodent oral acute toxicity (LD50) is a standard piece of information required to categorize chemicals in terms of the potential hazard posed to human health after acute exposure. The...

Authors: Domenico Gadaleta, Kristijan Vuković, Cosimo Toma, Giovanna J. Lavado, Agnes L. Karmaus, Kamel Mansouri, Nicole C. Kleinstreuer, Emilio Benfenati and Alessandra Roncaglioni

Flexible heuristic algorithm for automatic molecule fragmentation: application to the UNIFAC group contribution model

A priori calculation of thermophysical properties and predictive thermodynamic models can be very helpful for developing new industrial processes. Group contribution methods link the target property to contrib...

Authors: Simon Müller

PUG-View: programmatic access to chemical annotations integrated in PubChem

PubChem is a chemical data repository that provides comprehensive information on various chemical entities. It contains a wealth of chemical information from hundreds of data sources. Programmatic access to th...

Authors: Sunghwan Kim, Paul A. Thiessen, Tiejun Cheng, Jian Zhang, Asta Gindulyte and Evan E. Bolton

NaPLeS: a natural products likeness scorer—web application and database

Natural products (NPs), often also referred to as secondary metabolites, are small molecules synthesised by living organisms. Natural products are of interest due to their bioactivity and in this context as st...

Authors: Maria Sorokina and Christoph Steinbeck

Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping capability

This study aims at improving upon existing activity predictions methods by augmenting chemical structure fingerprints with bio-activity based fingerprints derived from high-throughput screening (HTS) data (HTS...

Authors: Oliver Laufkötter, Noé Sturm, Jürgen Bajorath, Hongming Chen and Ola Engkvist

DLIGAND2: an improved knowledge-based energy function for protein–ligand interactions using the distance-scaled, finite, ideal-gas reference state

Performance of structure-based molecular docking largely depends on the accuracy of scoring functions. One important type of scoring functions are knowledge-based potentials derived from known three-dimensiona...

Authors: Pin Chen, Yaobin Ke, Yutong Lu, Yunfei Du, Jiahui Li, Hui Yan, Huiying Zhao, Yaoqi Zhou and Yuedong Yang

Correction to: A new topological descriptor for water network structure

It was highlighted that the original article [1] was missing Additional files 2 and 3.

Authors: Lee Steinberg, John Russo and Jeremy Frey