Articles

DTi2Vec: Drug–target interaction prediction using network embedding and ensemble learning

Drug–target interaction (DTI) prediction is a crucial step in drug discovery and repositioning as it reduces experimental validation costs if done right. Thus, developing in-silico methods to predict potential DT...

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

Using informative features in machine learning based method for COVID-19 drug repurposing

Coronavirus disease 2019 (COVID-19) is caused by a novel virus named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus induced a large number of deaths and millions of confirmed cases wo...

Authors: Rosa Aghdam, Mahnaz Habibi and Golnaz Taheri

A hybrid framework for improving uncertainty quantification in deep learning-based QSAR regression modeling

Reliable uncertainty quantification for statistical models is crucial in various downstream applications, especially for drug design and discovery where mistakes may incur a large amount of cost. This topic ha...

Authors: Dingyan Wang, Jie Yu, Lifan Chen, Xutong Li, Hualiang Jiang, Kaixian Chen, Mingyue Zheng and Xiaomin Luo

Investigation of pharmacological mechanism of natural product using pathway fingerprints similarity based on “drug-target-pathway” heterogenous network

Natural products from traditional medicine inherit bioactivity from their source herbs. However, the pharmacological mechanism of natural products is often unclear and studied insufficiently. Pathway fingerpri...

Authors: Feifei Guo, Chunhong Jiang, Yujie Xi, Dan Wang, Yi Zhang, Ning Xie, Yi Guan, Fangbo Zhang and Hongjun Yang

Selecting lines for spectroscopic (re)measurements to improve the accuracy of absolute energies of rovibronic quantum states

Improving the accuracy of absolute energies associated with rovibronic quantum states of molecules requires accurate high-resolution spectroscopy measurements. Such experiments yield transition wavenumbers fro...

Authors: Péter Árendás, Tibor Furtenbacher and Attila G. Császár

2D SIFt: a matrix of ligand-receptor interactions

Depicting a ligand-receptor complex via Interaction Fingerprints has been shown to be both a viable data visualization and an analysis tool. The spectrum of its applications ranges from simple visualization of...

Authors: Stefan Mordalski, Agnieszka Wojtuch, Igor Podolak, Rafał Kurczab and Andrzej J. Bojarski

PUResNet: prediction of protein-ligand binding sites using deep residual neural network

Predicting protein-ligand binding sites is a fundamental step in understanding the functional characteristics of proteins, which plays a vital role in elucidating different biological functions and is a crucia...

Authors: Jeevan Kandel, Hilal Tayara and Kil To Chong

Computational Applications in Secondary Metabolite Discovery (CAiSMD): an online workshop

We report the major conclusions of the online open-access workshop “Computational Applications in Secondary Metabolite Discovery (CAiSMD)” that took place from 08 to 10 March 2021. Invited speakers from academ...

Authors: Fidele Ntie-Kang, Kiran K. Telukunta, Serge A. T. Fobofou, Victor Chukwudi Osamor, Samuel A. Egieyeh, Marilia Valli, Yannick Djoumbou-Feunang, Maria Sorokina, Conrad Stork, Neann Mathai, Paul Zierep, Ana L. Chávez-Hernández, Miquel Duran-Frigola, Smith B. Babiaka, Romuald Tematio Fouedjou, Donatus B. Eni…

Holistic evaluation of biodegradation pathway prediction: assessing multi-step reactions and intermediate products

The prediction of metabolism and biotransformation pathways of xenobiotics is a highly desired tool in environmental sciences, drug discovery, and (eco)toxicology. Several systems predict single transformation...

Authors: Jason Y. C. Tam, Tim Lorsbach, Sebastian Schmidt and Jörg S. Wicker

Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty

Measurements of protein–ligand interactions have reproducibility limits due to experimental errors. Any model based on such assays will consequentially have such unavoidable errors influencing their performanc...

Authors: Lewis H. Mervin, Maria-Anna Trapotsi, Avid M. Afzal, Ian P. Barrett, Andreas Bender and Ola Engkvist

DECIMER 1.0: deep learning for chemical image recognition using transformers

The amount of data available on chemical structures and their properties has increased steadily over the past decades. In particular, articles published before the mid-1990 are available only in printed or sca...

Authors: Kohulan Rajan, Achim Zielesny and Christoph Steinbeck

CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles

Ether-a-go-go-related gene (hERG) channel blockade by small molecules is a big concern during drug development in the pharmaceutical industry. Blockade of hERG channels may cause prolonged QT intervals that po...

Authors: Abdul Karim, Matthew Lee, Thomas Balle and Abdul Sattar

Learning protein-ligand binding affinity with atomic environment vectors

Scoring functions for the prediction of protein-ligand binding affinity have seen renewed interest in recent years when novel machine learning and deep learning methods started to consistently outperform class...

Authors: Rocco Meli, Andrew Anighoro, Mike J. Bodkin, Garrett M. Morris and Philip C. Biggin

GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data

Traditional techniques to identify macromolecular targets for drugs utilize solely the information on a query drug and a putative target. Nonetheless, the mechanisms of action of many drugs depend not only on...

Authors: Guannan Liu, Manali Singha, Limeng Pu, Prasanga Neupane, Joseph Feinstein, Hsiao-Chun Wu, J. Ramanujam and Michal Brylinski

QPHAR: quantitative pharmacophore activity relationship: method and validation

QSAR methods are widely applied in the drug discovery process, both in the hit‐to‐lead and lead optimization phase, as well as in the drug-approval process. Most QSAR algorithms are limited to using molecules ...

Authors: Stefan M. Kohlbacher, Thierry Langer and Thomas Seidel

MLSolvA: solvation free energy prediction from pairwise atomistic interactions by machine learning

Recent advances in machine learning technologies and their applications have led to the development of diverse structure–property relationship models for crucial chemical properties. The solvation free energy ...

Authors: Hyuntae Lim and YounJoon Jung

Ensemble completeness in conformer sampling: the case of small macrocycles

In this study we compare the three algorithms for the generation of conformer ensembles Biovia BEST, Schrödinger Prime macrocycle sampling (PMM) and Conformator (CONF) form the University of Hamburg, with ense...

Authors: Lea Seep, Anne Bonin, Katharina Meier, Holger Diedam and Andreas H. Göller

“Molecular Anatomy”: a new multi-dimensional hierarchical scaffold analysis tool

The scaffold representation is widely employed to classify bioactive compounds on the basis of common core structures or correlate compound classes with specific biological activities. In this paper, we presen...

Authors: Candida Manelfi, Marica Gemei, Carmine Talarico, Carmen Cerchia, Anna Fava, Filippo Lunghini and Andrea Rosario Beccari

Charged aerosol detector response modeling for fatty acids based on experimental settings and molecular features: a machine learning approach

The charged aerosol detector (CAD) is the latest representative of aerosol-based detectors that generate a response independent of the analytes’ chemical structure. This study was aimed at accurately predictin...

Authors: Ruben Pawellek, Jovana Krmar, Adrian Leistner, Nevena Djajić, Biljana Otašević, Ana Protić and Ulrike Holzgrabe

Correction to: Optimized SQE atomic charges for peptides accessible via a web application

A Correction to this paper has been published: https://​doi.​org/​10.​1186/​s13321-021-00528-w

Authors: Ondřej Schindler, Tomáš Raček, Aleksandra Maršavelski, Jaroslav Koča, Karel Berka and Radka Svobodová

MassSpecBlocks: a web-based tool to create building blocks and sequences of nonribosomal peptides and polyketides for tandem mass spectra analysis

Nonribosomal peptides and polyketides are natural products commonly synthesized by microorganisms. They are widely used in medicine, agriculture, environmental protection, and other fields. The structures of n...

Authors: Jan Přívratský and Jiří Novák

FAIR chemical structures in the Journal of Cheminformatics

The ability to access chemical information openly is an essential part of many scientific disciplines. The Journal of Cheminformatics is leading the way for rigorous, open cheminformatics in many ways, but the...

Authors: Emma L. Schymanski and Evan E. Bolton

Reply to “FAIR chemical structure in the Journal of Cheminformatics”

Authors: Rajarshi Guha, Nina Jeliazkova, Egon Willighagen and Barbara Zdrazil

MAYGEN: an open-source chemical structure generator for constitutional isomers based on the orderly generation principle

The generation of constitutional isomer chemical spaces has been a subject of cheminformatics since the early 1960s, with applications in structure elucidation and elsewhere. In order to perform such a generat...

Authors: Mehmet Aziz Yirik, Maria Sorokina and Christoph Steinbeck

Nonadditivity in public and inhouse data: implications for drug design

Numerous ligand-based drug discovery projects are based on structure-activity relationship (SAR) analysis, such as Free-Wilson (FW) or matched molecular pair (MMP) analysis. Intrinsically they assume linearity...

Authors: D. Gogishvili, E. Nittinger, C. Margreitter and C. Tyrchan

Twitter integration of chemistry software tools

Social media activity on a research article is considered to be an altmetric, a new measure to estimate research impact. Demonstrating software on Twitter is a powerful way to attract attention from a larger a...

Authors: Naruki Yoshikawa, Ryuichi Kubo and Kazuki Z. Yamamoto

Optimized SQE atomic charges for peptides accessible via a web application

Partial atomic charges find many applications in computational chemistry, chemoinformatics, bioinformatics, and nanoscience. Currently, frequently used methods for charge calculation are the Electronegativity ...

Authors: Ondřej Schindler, Tomáš Raček, Aleksandra Maršavelski, Jaroslav Koča, Karel Berka and Radka Svobodová

LigTMap: ligand and structure-based target identification and activity prediction for small molecular compounds

Target prediction is a crucial step in modern drug discovery. However, existing experimental approaches to target prediction are time-consuming and costly. Here, we introduce LigTMap, an online server with a f...

Authors: Faraz Shaikh, Hio Kuan Tai, Nirali Desai and Shirley W. I. Siu

GNINA 1.0: molecular docking with deep learning

Molecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness o...

Authors: Andrew T. McNutt, Paul Francoeur, Rishal Aggarwal, Tomohide Masuda, Rocco Meli, Matthew Ragoza, Jocelyn Sunseri and David Ryan Koes

A machine learning platform for the discovery of materials

Authors: Carl E. Belle, Vural Aksakalli and Salvy P. Russo

Benchmarks for interpretation of QSAR models

Interpretation of QSAR models is useful to understand the complex nature of biological or physicochemical processes, guide structural optimization or perform knowledge-based validation of QSAR models. Highly p...

Authors: Mariia Matveieva and Pavel Polishchuk

InChI version 1.06: now more than 99.99% reliable

The software for the IUPAC Chemical Identifier, InChI, is extraordinarily reliable. It has been tested on large databases around the world, and has proved itself to be an essential tool in the handling and int...

Authors: Jonathan M. Goodman, Igor Pletnev, Paul Thiessen, Evan Bolton and Stephen R. Heller

Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study

Deep generative models have shown the ability to devise both valid and novel chemistry, which could significantly accelerate the identification of bioactive compounds. Many current models, however, use molecul...

Authors: Morgan Thomas, Robert T. Smith, Noel M. O’Boyle, Chris de Graaf and Andreas Bender

IDSM ChemWebRDF: SPARQLing small-molecule datasets

The Resource Description Framework (RDF), together with well-defined ontologies, significantly increases data interoperability and usability. The SPARQL query language was introduced to retrieve requested RDF ...

Authors: Jakub Galgonek and Jiří Vondrášek

SANCDB: an update on South African natural compounds and their readily available analogs

South African Natural Compounds Database (SANCDB; https://​sancdb.​rubi.​ru.​ac.​za/​) is the sole and a fully referenced database of natural chemical compounds of...

Authors: Bakary N’tji Diallo, Michael Glenister, Thommas M. Musyoka, Kevin Lobb and Özlem Tastan Bishop

Deep learning integration of molecular and interactome data for protein–compound interaction prediction

Virtual screening, which can computationally predict the presence or absence of protein–compound interactions, has attracted attention as a large-scale, low-cost, and short-term search method for seed compound...

Authors: Narumi Watanabe, Yuuto Ohnuki and Yasubumi Sakakibara

Assessing the calibration in toxicological in vitro models with conformal prediction

Machine learning methods are widely used in drug discovery and toxicity prediction. While showing overall good performance in cross-validation studies, their predictive power (often) drops in cases where the q...

Authors: Andrea Morger, Fredrik Svensson, Staffan Arvidsson McShane, Niharika Gauraha, Ulf Norinder, Ola Spjuth and Andrea Volkamer

STOUT: SMILES to IUPAC names using neural machine translation

Chemical compounds can be identified through a graphical depiction, a suitable string representation, or a chemical name. A universally accepted naming scheme for chemistry was established by the International...

Authors: Kohulan Rajan, Achim Zielesny and Christoph Steinbeck

Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 2: speed, consistency, diversity selection

Despite being a central concept in cheminformatics, molecular similarity has so far been limited to the simultaneous comparison of only two molecules at a time and using one index, generally the Tanimoto coeff...

Authors: Ramón Alain Miranda-Quintana, Anita Rácz, Dávid Bajusz and Károly Héberger

Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 1: Theory and characteristics^†

Quantification of the similarity of objects is a key concept in many areas of computational science. This includes cheminformatics, where molecular similarity is usually quantified based on binary fingerprints...

Authors: Ramón Alain Miranda-Quintana, Dávid Bajusz, Anita Rácz and Károly Héberger

Flame: an open source framework for model development, hosting, and usage in production environments

This article describes Flame, an open source software for building predictive models and supporting their use in production environments. Flame is a web application with a web-based graphic interface, which ca...

Authors: Manuel Pastor, José Carlos Gómez-Tamayo and Ferran Sanz

Multi-PLI: interpretable multi‐task deep learning model for unifying protein–ligand interaction datasets

The assessment of protein–ligand interactions is critical at early stage of drug discovery. Computational approaches for efficiently predicting such interactions facilitate drug development. Recently, methods ...

Authors: Fan Hu, Jiaxin Jiang, Dongqi Wang, Muchun Zhu and Peng Yin

QSAR-Co-X: an open source toolkit for multitarget QSAR modelling

Quantitative structure activity relationships (QSAR) modelling is a well-known computational tool, often used in a wide variety of applications. Yet one of the major drawbacks of conventional QSAR modelling is...

Authors: Amit Kumar Halder and M. Natália Dias Soeiro Cordeiro

SMPLIP-Score: predicting ligand binding affinity from simple and interpretable on-the-fly interaction fingerprint pattern descriptors

In drug discovery, rapid and accurate prediction of protein–ligand binding affinities is a pivotal task for lead optimization with acceptable on-target potency as well as pharmacological efficacy. Furthermore,...

Authors: Surendra Kumar and Mi-hyun Kim

Analysis of the effects of related fingerprints on molecular similarity using an eigenvalue entropy approach

Two-dimensional (2D) chemical fingerprints are widely used as binary features for the quantification of structural similarity of chemical compounds, which is an important step in similarity-based virtual scree...

Authors: Hiroyuki Kuwahara and Xin Gao

Molecular optimization by capturing chemist’s intuition using deep neural networks

A main challenge in drug discovery is finding molecules with a desirable balance of multiple properties. Here, we focus on the task of molecular optimization, where the goal is to optimize a given starting mol...

Authors: Jiazhen He, Huifang You, Emil Sandström, Eva Nittinger, Esben Jannik Bjerrum, Christian Tyrchan, Werngard Czechtizky and Ola Engkvist

Development and evaluation of two-parameter linear free energy models for the prediction of human skin permeability coefficient of neutral organic chemicals

The experimental values of skin permeability coefficients, required for dermal exposure assessment, are not readily available for many chemicals. The existing estimation approaches are either less accurate or ...

Authors: Sana Naseem, Yasuyuki Zushi and Deedar Nabi

MolFinder: an evolutionary algorithm for the global optimization of molecular properties and the extensive exploration of chemical space using SMILES

Here, we introduce a new molecule optimization method, MolFinder, based on an efficient global optimization algorithm, the conformational space annealing algorithm, and the SMILES representation. MolFinder fin...

Authors: Yongbeom Kwon and Juyong Lee

Learning chemistry: exploring the suitability of machine learning for the task of structure-based chemical ontology classification

Chemical data is increasingly openly available in databases such as PubChem, which contains approximately 110 million compound entries as of February 2021. With the availability of data at such scale, the burd...

Authors: Janna Hastings, Martin Glauer, Adel Memariani, Fabian Neuhaus and Till Mossakowski

ChemProps: A RESTful API enabled database for composite polymer name standardization

The inconsistency of polymer indexing caused by the lack of uniformity in expression of polymer names is a major challenge for widespread use of polymer related data resources and limits broad application of m...

Authors: Bingyin Hu, Anqi Lin and L. Catherine Brinson