Articles

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

Accuracy evaluation and addition of improved dihedral parameters for the MMFF94s

The Platinum dataset of protein-bound ligand conformations was used to benchmark the ability of the MMFF94s force field to generate bioactive conformations by minimization of randomly generated conformers. Tor...

Authors: Joel Wahl, Joel Freyss, Modest von Korff and Thomas Sander

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

Rapid prediction of NMR spectral properties with quantified uncertainty

Accurate calculation of specific spectral properties for NMR is an important step for molecular structure elucidation. Here we report the development of a novel machine learning technique for accurately predic...

Authors: Eric Jonas and Stefan Kuhn

Fast, efficient fragment-based coordinate generation for Open Babel

Rapidly predicting an accurate three dimensional geometry of a molecule is a crucial task for cheminformatics and across a wide range of molecular modeling. Consequently, developing a fast, accurate, and open ...

Authors: Naruki Yoshikawa and Geoffrey R. Hutchison

A new topological descriptor for water network structure

Bulk water molecular dynamics simulations based on a series of atomistic water potentials (TIP3P, TIP4P/Ew, SPC/E and OPC) are compared using new techniques from the field of topological data analysis. The top...

Authors: Lee Steinberg, John Russo and Jeremy Frey

Bioactivity-explorer: a web application for interactive visualization and exploration of bioactivity data

To better leverage the accumulated bioactivity data in the ChEMBL database, we have developed Bioactivity-explorer, a web application for interactive visualization and exploration of the large-scale bioactivit...

Authors: Lu Liang, Chunfeng Ma, Tengfei Du, Yufei Zhao, Xiaoyong Zhao, Mengmeng Liu, Zhonghua Wang and Jianping Lin

Multi-channel PINN: investigating scalable and transferable neural networks for drug discovery

Analysis of compound–protein interactions (CPIs) has become a crucial prerequisite for drug discovery and drug repositioning. In vitro experiments are commonly used in identifying CPIs, but it is not feasible ...

Authors: Munhwan Lee, Hyeyeon Kim, Hyunwhan Joe and Hong-Gee Kim

Journal of Cheminformatics, ORCID, and GitHub

Authors: Egon Willighagen, Nina Jeliazkova and Rajarshi Guha

Interoperable chemical structure search service

The existing connections between large databases of chemicals, proteins, metabolites and assays offer valuable resources for research in fields ranging from drug design to metabolomics. Transparent search acro...

Authors: Miroslav Kratochvíl, Jiří Vondrášek and Jakub Galgonek

Cheminformatics approach to exploring and modeling trait-associated metabolite profiles

Developing predictive and transparent approaches to the analysis of metabolite profiles across patient cohorts is of critical importance for understanding the events that trigger or modulate traits of interest...

Authors: Jeremy R. Ash, Melaine A. Kuenemann, Daniel Rotroff, Alison Motsinger-Reif and Denis Fourches

Next generation community assessment of biomedical entity recognition web servers: metrics, performance, interoperability aspects of BeCalm

Shared tasks and community challenges represent key instruments to promote research, collaboration and determine the state of the art of biomedical and chemical text mining technologies. Traditionally, such ta...

Authors: Martin Pérez-Pérez, Gael Pérez-Rodríguez, Aitor Blanco-Míguez, Florentino Fdez-Riverola, Alfonso Valencia, Martin Krallinger and Anália Lourenço

KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images

The application of convolutional neural networks (ConvNets) to harness high-content screening images or 2D compound representations is gaining increasing attention in drug discovery. However, existing applicat...

Authors: Isidro Cortés-Ciriano and Andreas Bender

DockRMSD: an open-source tool for atom mapping and RMSD calculation of symmetric molecules through graph isomorphism

Comparison of ligand poses generated by protein–ligand docking programs has often been carried out with the assumption of direct atomic correspondence between ligand structures. However, this correspondence is...

Authors: Eric W. Bell and Yang Zhang

Improved algorithms for quantifying the near symmetry of proteins: complete side chains analysis

Symmetry of proteins, an important source of their elegant structure and unique functions, is not as perfect as it may seem. In the framework of continuous symmetry, in which symmetry is no longer a binary yes...

Authors: Inbal Tuvi-Arad and Gil Alon

An integrated quantitative structure and mechanism of action-activity relationship model of human serum albumin binding

Traditional quantitative structure-activity relationship models usually neglect the molecular alterations happening in the exposed systems (the mechanism of action, MOA), that mediate between structural proper...

Authors: Angela Serra, Serli Önlü, Pietro Coretto and Dario Greco

ErtlFunctionalGroupsFinder: automated rule-based functional group detection with the Chemistry Development Kit (CDK)

The Ertl algorithm for automated functional groups (FG) detection and extraction of organic molecules is implemented on the basis of the Chemistry Development Kit (CDK). A distinct impact of the chosen CDK aro...

Authors: Sebastian Fritsch, Stefan Neumann, Jonas Schaub, Christoph Steinbeck and Achim Zielesny

Leveraging heterogeneous data from GHS toxicity annotations, molecular and protein target descriptors and Tox21 assay readouts to predict and rationalise acute toxicity

Despite the increasing knowledge in both the chemical and biological domains the assimilation and exploration of heterogeneous datasets, encoding information about the chemical, bioactivity and phenotypic prop...

Authors: Chad H. G. Allen, Lewis H. Mervin, Samar Y. Mahmoud and Andreas Bender

An exploration strategy improves the diversity of de novo ligands using deep reinforcement learning: a case for the adenosine A_2A receptor

Over the last 5 years deep learning has progressed tremendously in both image recognition and natural language processing. Now it is increasingly applied to other data rich fields. In drug discovery, recurrent...

Authors: Xuhan Liu, Kai Ye, Herman W. T. van Vlijmen, Adriaan P. IJzerman and Gerard J. P. van Westen

Gypsum-DL: an open-source program for preparing small-molecule libraries for structure-based virtual screening

Computational techniques such as structure-based virtual screening require carefully prepared 3D models of potential small-molecule ligands. Though powerful, existing commercial programs for virtual-library pr...

Authors: Patrick J. Ropp, Jacob O. Spiegel, Jennifer L. Walker, Harrison Green, Guillermo A. Morales, Katherine A. Milliken, John J. Ringe and Jacob D. Durrant

Capturing mixture composition: an open machine-readable format for representing mixed substances

We describe a file format that is designed to represent mixtures of compounds in a way that is fully machine readable. This Mixfile format is intended to fill the same role for substances that are composed of mul...

Authors: Alex M. Clark, Leah R. McEwen, Peter Gedeck and Barry A. Bunin