26 papers · ranked by Valyu relevance
Janna Hastings, Leonid Chepelev, Egon Willighagen, Nico Adams + 3 more
Cheminformatics is the application of informatics techniques to solve chemical problems in silico. There are many areas in biology where cheminformatics plays an important role in computational research, including metabolism, proteomics, and systems biology. One critical aspect in the application of cheminformatics in…
David J Wild
Editorial Welcome to the Journal of Cheminformatics. We are proud to be associated with a field that has a history longer than most applied computational disciplines; that has elegantly solved so many basic (and not so basic) problems; that has a reputation for intellectual rigor and good-naturedness; that has hundreds…
Authors not listed
The widespread adoption of open-source cheminformatics toolkits remains constrained by technical implementation barriers, including complex installation procedures, dependency management, and integration challenges. Here, we present Cheminformatics Microservice V3, a significant update to the existing platform that…
Julian E Fuchs, Andreas Bender, Robert C Glen
The Centre for Molecular Informatics, formerly Unilever Centre for Molecular Science Informatics (UCMSI), at the University of Cambridge is a world-leading driving force in the field of cheminformatics. Since its opening in 2000 more than 300 scientific articles have fundamentally changed the field of molecular…
Venkata Chandrasekhar, Nisha Sharma, Jonas Schaub, Christoph Steinbeck + 1 more
In recent years, cheminformatics has experienced significant advancements through the development of new open-source software tools based on various cheminformatics programming toolkits. However, adopting these toolkits presents challenges, including proper installation, setup, deployment, and compatibility management.…
Rajarshi Guha, Egon Willighagen
As we take over the editorial reigns of J. Cheminform. we would like to present our goals for the journal and our views on where cheminformatics as a field is headed and how the journal may influence it. As editors our overarching goal is to disseminate cheminformatics research and practice that is impactful and useful…
Kohulan Rajan, Venkata Chandrasekhar, Nisha Sharma, Sri Ram Sagar Kanakam + 2 more
The widespread adoption of open-source cheminformatics toolkits remains constrained by technical implementation barriers, including complex installation procedures, dependency management, and integration challenges. Here, we present Cheminformatics Microservice V3, a significant update to the existing platform that…
Prasannavenkatesh Durai, Daniel P. Russo, Yitao Shen, Tong Wang + 3 more
Chemical toxicity assessment is critical for drug development and environmental safety. Computational models have emerged as a promising alternative to animal testing and now play a significant role in efficiently evaluating new chemicals. To address the urgent need for providing user-friendly machine learning tools in…
Authors not listed
In chemoinformatics, chemical databases have great importance since their main objective is to store and organize the chemical structures of molecules and their properties, from basic information such as chemical structure to more complex like molecular fingerprints or other types of calculated or experimental…
Jean-Louis Reymond
Chemistry has diversified from a basic understanding of the elements to studying millions of highly diverse molecules and materials, which together are conceptualized as the chemical space. A map of this chemical space where distances represent similarities between compounds can represent the mutual relationships…
Shaherin Basith, Minghua Cui, Stephani J. Y. Macalino, Jongmi Park + 3 more
'Nina A. B. Clavio' 'Soosung Kang' 'Sun Choi'] The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational…
Stefan Bräse
Digital chemistry represents a transformative approach integrating computational methods, digital data, and automation within the chemical sciences. It is defined by the use of digital tools and algorithms to simulate, predict, accelerate, and analyze chemical processes and properties, augmenting traditional…
Jeremy R. Ash Jacqueline M. Hughes-Oliver
The goal of chemmodlab is to streamline the fitting and assessment pipeline for many machine learning models in R, making it easy for researchers to compare the utility of new models. While focused on implementing methods for model fitting and assessment that have been accepted by experts in the cheminformatics field…
Dionisio A. Olmedo, Armando A. Durant-Archibold, José Luis López-Pérez, Jose L. Medina-Franco
Chemical libraries and compound data sets are among the main inputs to start the drug discovery process at universities, research institutes, and the pharmaceutical industry. The approach used in the design of compound libraries, the chemical information they possess, and the representation of structures, play a…
Kenneth Lopez Perez, Edgar López-López, Flavie Soulage, Eloy Felix + 2 more
Chemical space is a core and theoretical concept in cheminformatics, and it also has practical applications in drug discovery and other research areas. Chemical space is frequently associated with the number of molecules in the universe (e.g., chemical universe). It is well known that the number of compounds (both…
Daven Lim, Swathi Badrinarayanan, Kira Sterling, Guru Rajesh + 10 more
Recent advancements in large language models (LLMs) offer new opportunities for automating the manual curation of biochemical reaction databases from scientific literature. In this study, we present an integrated pipeline that enhances LLM-based extraction of enzymatic reactions with machine learning and…
JJ Ben-Joseph, Tim Oates
We have developed an approach to train a chemical property prediction model using both English and the SELFIES chemical language describing the structure of small, drug-like molecules. This model generates chemical embedding vectors, which we then use to train classification models. Our straightforward softmax…
Staffan Arvidsson McShane, Ulf Norinder, Jonathan Alvarsson, Ernst Ahlberg + 2 more
Conformal prediction has seen many applications in pharmaceutical science, being able to calibrate outputs of machine learning models and producing valid prediction intervals. We here present the open source software CPSign that is a complete implementation of conformal prediction for cheminformatics modeling. CPSign…
Tobias Kind, Tim Leamy, Julie A Leary, Oliver Fiehn
Background Modern chemistry laboratories operate with a wide range of software applications under different operating systems, such as Windows, LINUX or Mac OS X. Instead of installing software on different computers it is possible to install those applications on a single computer using Virtual Machine software.…
Stanisław Jastrzȩbski, Damian Leśniak, Wojciech Marian Czarnecki
This paper shows how one can directly apply natural language processing (NLP) methods to classification problems in cheminformatics. Connection between these seemingly separate fields is shown by considering standard textual representation of compound, SMILES. The problem of activity prediction against a target protein…
Oliver Goldstein
The Algebraic Data Type (ADT) can be used as a computational framework for molecular representation for the purpose of advancing tasks in cheminformatics. This can include generative modles in the context of Bayesian machine learning via probabilistic programming. The ADT that we put forward, implements the 'Dietz'…
Austin Clyde, Ashka Shah, Max Zvyagin, Arvind Ramanathan + 1 more
'Rick Stevens'] Scaffold based drug discovery (SBDD) is a technique for drug discovery which pins chemical scaffolds as the framework of design. Scaffolds, or molecular frameworks, organize the design of compounds into local neighborhoods. We formalize scaffold based drug discovery into a network design. Utilizing…
Monee McGrady, Sean Colby, Jamie R Nuñez, Ryan Renslow + 1 more
When considering large sets of molecules, it is helpful to place them in the context of a "chemical space" – a multidimensional space defined by a set of descriptors (e.g., molecular properties) that can be used to visualize and analyze compound grouping as well as identify regions that might be void of valid…
Daniela Gaytán-Hernández, Ana L. Chávez-Hernández, Edgar López-López, Jazmín Miranda-Salas + 2 more
Science and art have been connected for centuries. With the development of new computational methods, new scientific disciplines have emerged, such as computational chemistry, and related fields, such as chemoinformatics that use informatic methods to solve chemical problems focusing on small molecules.…
Nikita Janakarajan, Tim Erdmann, Sarath Swaminathan, Teodoro Laino + 1 more
'Jannis Born'] The success of language models, especially transformer-based architectures, has trickled into other domains giving rise to "scientific language models" that operate on small molecules, proteins or polymers. In chemistry, language models contribute to accelerating the molecule discovery cycle as evidenced…
Ana Luísa Teixeira, Rui C. Santos, João Paulo Leal, José A. Martinho Simões + 1 more
'José A. Martinho Simões' 'André O. Falcão'] Standard enthalpies of formation are used for assessing the efficiency and safety of chemical processes in the chemical industry. However, the number of compounds for which the enthalpies of formation are available is many orders of magnitude smaller than the number of known…