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Increasingly more complex problems are being tackled by computational approaches in biological and medicinal chemistry. Specifically, computational analysis and prediction is no longer limited to single ligand-receptor interactions, but poly-pharmacological and other multi-target effects and their underlying molecular mechanisms have become a focal point of scientific research. Molecular Informatics provides the ideal publication forum for these kinds of studies. This is reflected in the spectrum of topics covered by the journal. In 2013, Molecular Informatics published a total of 82 critically pre-screened and peer-reviewed Full Papers, Communications, Reviews, and Methods Corner articles contributed by authors from across the globe. The published articles originated from Germany (15 % of articles), USA (12 %), China (11 %), as well as 27 further countries. Three timely Special Issues featured Advances in Computational Toxicology (issue 1/2013, guest editor: Thomas Steger-Hartmann), selected highlights of the 19th European Symposium on Quantitative Structure-Activity Relationships (EuroQSAR 2012) focusing on knowledge-enabled ligand design (issue 5–6/2013), and Chemogenomics (issue 11–12/2013, guest editor: Steffen Renner). All Special Issues were very well received by the readers of the journal, and we will continue publishing special topically focused compilations in 2014. After careful and critical review of all Full Papers published in Molecular Informatics in 2013, it is our great pleasure to announce the winners of the Editors’ Choice “2013 Best Paper Award”. The recipients of the 2013 award are Daniel Muthas and Scott Boyer for their original contribution entitled “Exploiting Pharmacological Similarity to Identify Safety Concerns – Listen to What the Data Tells You”.1 The whole editorial team congratulates the authors! At the same time, we look forward to receiving numerous high-quality manuscripts to compete for the “2014 Best Paper Award”. We would like to express our great gratitude to all authors, Editorial Advisory Board members, and peer reviewers for their continuing support. At the same time we bid farewell to some of our Editorial Advisory Board members and welcome the new members: Dmitry A. Konovalov (James Cook University) and Tanja Weil (Universität Ulm) finished their terms at the end of 2013, and Jürgen Bajorath (Universität Bonn), Frank Böckler (Universität Tübingen), Didier Rognan (CNRS UMR), David Winkler (CSIRO Materials Science and Engineering), and Gerhard Wolber (Freie Universität Berlin) join the Board from 2014. The persistent high quality of the contributions published in Molecular Informatics is reflected by its sustained impact factor of 2.34. With full commitment, the editors will continue to consequently strengthen the journal’s position as a premier publication forum at the forefront of cheminformatics and computational medicinal chemistry. As an important and pertinent measure to ensure future success and increase the journal′s visibility Molecular Informatics will be published as a full-fledged online journal starting with its first issue in 2014. The new format will enable access to state-of-the-art electronic publishing and thereby address the requirements of modern scientific exchange and knowledge dissemination within the journal′s scope. All articles will be published without charging authors for manuscript handling and review, color reproduction or per page costs. At the same time the journal′s print production will be stopped, making a relevant contribution to ecologic publishing. Among the exciting new features, Molecular Informatics will offer a Molecular Informatics app for iPads that will provide a powerful new browsing and reading experience of the journal. Another major development will be the Article Anywhere format, which will make the HTML version of each article even more functional, easier to navigate, and more compatible with mobile reading devices. We look forward to receiving your best manuscripts for publication. Molecular Informatics. Going Fully Online …︁ now! 1 1 1 1 Knut Baumann Gerhard Ecker Jordi Mestres Gisbert Schneider The Editors

As a scientific researcher one might feel invigorated reading about the recent excitement caused by articles on computational “Big Data” analysis and “Deep Learning” methods. A world of seemingly endless new opportunities for computer applications seems to have suddenly opened up. Without doubt, there is a continuously increasing demand for computational project support in the life sciences. Still, one may wonder if all of the perceived enthusiasm is realistic. A healthy skepticism seems appropriate when reading some of the articles available on the topic. The readers of Molecular Informatics will have noted that we have, as one of the scientific first journals, published several seminal papers on this topic, and we intend to strengthen our trend-setting position. Thorough analyses of both the algorithms and their applications, together with a critical assessment of the opportunities and challenges of this emerging field of research, seem imperative. Advanced machine learning methods have become mainstream in computational drug discovery. This is reflected by the continuously increasing number of publications in this area. Molecular Informatics is committed to provide the most appropriate publication forum for these interdisciplinary research studies. We therefore invite you to submit contributions to Molecular Informatics on the topics of big data and deep learning methods and their applications. Since 2012, the “Molecular Informatics Best Paper Award” has been given to distinguish excellent publications in our journal. It is an annual award reflecting the development of the field, and it is based on the editors' choice, taking into account criteria such as novelty, topic, online usage, and impact. We are delighted to announce that the Editor's choice for the 2016 “Molecular Informatics Best Paper Award” goes to the paper entitled “Predictive Models for Halogen-bond Basicity of Binding Sites of Polyfunctional Molecules” led by Alexandre Varnek, from the Laboratoire de Chémoinformatique at Université de Strasbourg, France.1 Congratulations to the whole team of authors of this work! We would like to express our gratitude to all authors, peer reviewers, and Editorial Advisory Board members who contributed to Molecular Informatics during the past year and help us to keep its high standards. We are particularly grateful to Gerhard Ecker and Jordi Mestres, who, having served as dedicated editors of Molecular Informatics for many years, decided to make place for a renewed team of editors. We are delighted to welcome Sourav Das (St. Jude Children's Research Hospital, Memphis, TN, USA) and Yoshihiro Yamanishi (Kyushu University, Japan) as Associate Editors. Molecular Informatics has become a leading journal covering all aspects of chem- and bioinformatics, and we will continue to work towards strengthening the journal's position in our community.

Recent years have seen a sharp increase in the development of deep learning and artificial intelligence-based molecular informatics. There has been a growing interest in applying deep learning to several subfields, including the digital transformation of synthetic chemistry, extraction of chemical information from the scientific literature, and AI in natural product-based drug discovery. The application of AI to molecular informatics is still constrained by the fact that most of the data used for training and testing deep learning models are not available as FAIR and open data. As open science practices continue to grow in popularity, initiatives which support FAIR and open data as well as open-source software have emerged. It is becoming increasingly important for researchers in the field of molecular informatics to embrace open science and to submit data and software in open repositories. With the advent of open-source deep learning frameworks and cloud computing platforms, academic researchers are now able to deploy and test their own deep learning models with ease. With the development of new and faster hardware for deep learning and the increasing number of initiatives towards digital research data management infrastructures, as well as a culture promoting open data, open source, and open science, AI-driven molecular informatics will continue to grow. This review examines the current state of open data and open algorithms in molecular informatics, as well as ways in which they could be improved in future.

Molecular Informatics utilises many ideas and concepts to find relationships between molecules. The concept of similarity, where molecules may be grouped according to their biological effects or physicochemical properties has found extensive use in drug discovery. Some areas of particular interest have been in lead discovery and compound optimisation. For example, in designing libraries of compounds for lead generation, one approach is to design sets of compounds "similar" to known active compounds in the hope that alternative molecular structures are found that maintain the properties required while enhancing e.g. patentability, medicinal chemistry opportunities or even in achieving optimised pharmacokinetic profiles. Thus the practical importance of the concept of molecular similarity has grown dramatically in recent years. The predominant users are pharmaceutical companies, employing similarity methods in a wide range of applications e.g. virtual screening, estimation of absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) and prediction of physicochemical properties (solubility, partitioning etc.). In this perspective, we discuss the representation of molecular structure (descriptors), methods of comparing structures and how these relate to measured properties. This leads to the concept of molecular similarity, its various definitions and uses and how these have evolved in recent years. Here, we wish to evaluate and in some cases challenge accepted views and uses of molecular similarity. Molecular similarity, as a paradigm, contains many implicit and explicit assumptions in particular with respect to the prediction of the binding and efficacy of molecules at biological receptors. The fundamental observation is that molecular similarity has a context which both defines and limits its use. The key issues of solvation effects, heterogeneity of binding sites and the fundamental problem of the form of similarity measure to use are addressed.

Currently, statins, the -hydroxy--methyl-glutaryl-CoA reductase (HMG-R) inhibitors, are widely used to lower cholesterol, nevertheless, they have several side effects.Consequently, the present study is designed to unravel the cardioprotective role of selected natural monoterpenoids (carvacrol and geraniol) via in-vitro targeting and molecular informatics study of HMG-R.Computational molecular informatics study revealed that carvacrol and geraniol efficiently occupies the catalytic site of HMG-R with the binding affinity ( G) of -4.60, and -1.99 Kcal/mol, respectively, and molecular mechanical-generalized Born surface area (MM-GBSA) free binding energy was depicted as -17.05 and -29.48 Kcal/mol, respectively.Further, molecular dynamics simulation was carried out for 100 ns.Carvacrol and geraniol potentially and competitively inhibit the in-vitro HMG-R activity with an IC 50 value of 78.23 2.21 M, and 72.91 2.92 M, respectively.Thus, both carvacrol and geraniol exhibited significant anti-hypercholesterolemic activity while the molecular simulation studies depicted that the GR complex showed better stability than the carvacrol complex.

Biomedical sensors have revolutionized healthcare by offering advanced tools for real-time disease diagnosis, continuous monitoring, and effective treatment. These sensors encompass various technologies, including biosensors, nanotechnology-based sensors, flexible and wearable devices, and more, all of which have played a pivotal role in modernizing medical practices. This review delves into the evolution, types, and technological advancements, along with their broad applications in healthcare, such as in molecular informatics, genomics, proteomics, and personalized medicine. The primary objective of this review is to provide a comprehensive overview of the current landscape, highlighting the latest technological innovations, diverse applications, and potential future trends. The review seeks to demonstrate the critical role these sensors play in enhancing diagnostic accuracy, treatment efficacy, and overall patient care. This review methodically examines various biomedical sensor technologies, focusing on advancements such as microfabrication techniques, nanotechnology, and the development of biocompatible materials. The analysis also covers the miniaturization of sensors, their portability, and the integration of these devices with the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) to improve healthcare outcomes. These sensors are widely used in wearable and implantable devices for continuous monitoring of vital signs, disease diagnosis, and therapeutic interventions. Notable applications include diabetes management, cardiovascular monitoring, and cancer detection. Emerging trends are set to further enhance the capabilities and applications of these sensors. Despite challenges such as biocompatibility, data privacy, and regulatory hurdles, ongoing research and innovation will likely overcome these barriers, ensuring that biomedical sensors, improve patient outcomes, and facilitate more personalized medical interventions.

The molecular electron density carries the complete information about the molecule. This information is stored in the shape and more general topological features of molecular electron densities. A fundamental relation of molecular informatics, building on the Hohenberg-Kohn theorem, is the holographic electron density theorem: any nonzero volume part of a molecular electron density in a non-degenerate electronic ground state contains the complete information about all properties of the entire molecule. This fundamental feature of all molecules applies to all exhibited and also to all latent molecular properties, where latent properties are those not normally exhibited, only in response to some external stimulus. Recently it has become feasible to compute ab initio quality electron densities and approximate forces acting on individual nuclei in large molecules, even those beyond the thousand atom range, such as proteins. The newly expanded size range where reliable modelling methods can be also applied extends the role of detailed molecular shape analysis to macromolecules. In this context, it has become possible to study how the fundamental information-carrying properties of electron density take a newly recognized role influencing the predominance of specific nuclear conformations within the family of astronomically many potentially stable conformations of some macromolecules. Some special problems and results are discussed.

Let’s put a ‘toe in the water’ of molecular informatics. There are 50 million or so accessible chemical substances, around 6 million available reagents, 7 million published chemical reactions, as well as nearly 16,000 protein X-ray crystal structures and 250,000 readily available small molecule X-ray structures. This is the tip of a large (and growing) information iceberg. One of the biggest challenges (and opportunities) in the chemical sciences today is how best to manage the mountains of data and information associated with compounds and their structures. Unilever and the University of Cambridge have set out to address this problem, in a unique partnership. The Unilever Centre for Molecular Informatics at the University of Cambridge is dedicated to the exciting new discipline of molecular informatics, under the leadership of Robert Glen, formerly Vice President of Collaborative Research at Tripos Inc. (St Louis, Missouri), a leading company in life sciences software. Professor Glen previously set up the Computer-aided Molecular Design group at the Wellcome Foundation; he is the co-inventor of the migraine drug, Zomig (AstraZeneca), and of two other compounds that have entered into Phase 2 clinical trials.

The molecular informatics platform, as implemented today in the Molecular and Library Informatics (MLI) Technology Program at Novartis Institutes for BioMedical Research (NIBR) Discovery Technologies, will be presented. The mission of the MLI program is primarily defined to contribute to the selection of screening hit and lead compounds using in silico methods. The MLI technology program aims to provide an integrated pipeline of computational methods for high-throughput in silico screening combining specific cheminformatics, bioinformatics, docking and 3D pharmacophore applications. The four core activities of the group include: 1) Molecular diversity management; 2) In silico screening using HTD (high-throughput docking) and 3D pharmacophore searching; 3) Integrated analysis of HTS (high-throughput screening) and profiling data; and 4) Database management and software engineering in the field of in silico screening. The contribution of these activities to the drug discovery process will be summarized together with novel trends in the field.

Natural products (NPs) remain the single most prolific source of inspiration in the development of functional small molecules, in particular of drugs.1 Researching NPs, however, is a non-trivial task, beginning with the sourcing, transfer and handling of materials for testing, and extending to the production of extracts, isolation of compounds, biological testing and identification of viable synthetic routes.2, 3 In this context, a lot of hope lies in the use of computational methods to provide guidance to experimentalists, allowing them to direct the available resources to the most promising avenues of research. Recent surveys have found a rich body of literature on the successful and broad application of cheminformatics approaches in NP-based drug discovery.4, 5 The most common applications include (i) data analysis and visualization, (ii) NP dereplication, (iii) quantification of NP-likeness, (iv) generation of synthetically accessible mimics of NPs, (v) analysis and prediction of the structural basis for the interaction of NPs with proteins, (vi) virtual screening for bioactive NPs, (vii) prediction of the macromolecular targets of NPs (target prediction), and (viii) prediction of ADME properties and toxicity. However, the structural complexity of many NPs (3D molecular shape complexity, stereochemistry, ring complexity, etc.) poses significant challenges to computational approaches, and the fact that most in silico approaches are designed for, or trained on, synthetic compounds is all too often overlooked. The challenges and opportunities involved in computer-guided NP research have fueled extensive interest in the development and application of new in silico methods, and we consider it timely to dedicate a special issue of Molecular Informatics to the topic. The contributions of this special issue cover (i) different methods for the analysis and visualization of the chemical space of NPs based on full molecular structures, scaffolds and fragments, (ii) the development and analysis of virtual collections of NPs and NP fragments, (iii) a review of pharmacophore-based approaches and their application to NP-based drug discovery, and (iv) examples of the successful application of target prediction methods and docking in the discovery and research of bioactive NPs. On behalf of all contributors to this special issue, I wish you an enjoyable and interesting read!

Chemoinformatics has been successfully employed in safety assessment through various regulatory programs for which information from databases, as well as predictive methodologies including computational methods, are accepted. One example is the European Union Cosmetics Products Regulations, for which Cosmetics Europe (CE) research activities in non-animal methods have been managed by the Long Range Science Strategy (LRSS) program. The vision is to use mechanistic aspects of existing non-animal methods, as well as New Approach Methodologies (NAMs), to demonstrate that safety assessment of chemicals can be performed using a combination of in silico and in vitro data. To this end, ChemTunes•ToxGPS® has been adopted as the foundation of the safety assessment system and provides a platform to integrate data and knowledge, and enable toxicity predictions and safety assessments, relevant to cosmetics industries. The ChemTunes•ToxGPS® platform provides chemical, biological, and safety data based both on experiments and predictions, and an interactive/customizable read-across platform. The safety assessment workflow enables users to compile qualified data sources, quantify their reliabilities, and combine them using a weight of evidence approach based on decision theory. The power of this platform was demonstrated through a use case to perform a safety assessment for Perilla frutescens through the workflows of threshold of toxicological concern (TTC), in silico predictions (QSAR and structural rules) and quantitative read-across (qRAX) assessment for overall safety. The system digitalizes workflows within a knowledge hub, exploiting advanced in silico tools in this age of artificial intelligence. The further design of the system for next generation risk assessment (NGRA) is scientifically guided by interactions between the workgroup and international regulatory entities.