Chemistry in Times of Artificial Intelligence

Gasteiger, Johann

doi:10.1002/cphc.202000518

Cited by 68 publications

(50 citation statements)

References 66 publications

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“…The capacity of CADD has further increased by the development of powerful machine learning approaches, deep learning in particular. 1 In recent years, software for CADD has become more widely accessible. Today, a range of software packages are available that are open-source or free to use for academic research.…”

Section: Computer-aided Drug Discoverymentioning

confidence: 99%

Rationality over fashion and hype in drug design

et al. 2021

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The current hype associated with machine learning and artificial intelligence often confuses scientists and students and may lead to uncritical or inappropriate applications of computational approaches. Even the field of computer-aided drug design (CADD) is not an exception. The situation is ambivalent. On one hand, more scientists are becoming aware of the benefits of learning from available data and are beginning to derive predictive models before designing experiments. However, on the other hand, easy accessibility of in silico tools comes at the risk of using them as “black boxes” without sufficient expert knowledge, leading to widespread misconceptions and problems. For example, results of computations may be taken at face value as “nothing but the truth” and data visualization may be used only to generate “pretty and colorful pictures”. Computational experts might come to the rescue and help to re-direct such efforts, for example, by guiding interested novices to conduct meaningful data analysis, make scientifically sound predictions, and communicate the findings in a rigorous manner. However, this is not always ensured. This contribution aims to encourage investigators entering the CADD arena to obtain adequate computational training, communicate or collaborate with experts, and become aware of the fundamentals of computational methods and their given limitations, beyond the hype. By its very nature, this Opinion is partly subjective and we do not attempt to provide a comprehensive guide to the best practices of CADD; instead, we wish to stimulate an open discussion within the scientific community and advocate rational rather than fashion-driven use of computational methods. We take advantage of the open peer-review culture of F1000Research such that reviewers and interested readers may engage in this discussion and obtain credits for their candid personal views and comments. We hope that this open discussion forum will contribute to shaping the future practice of CADD.

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Section: Computer-aided Drug Discoverymentioning

confidence: 99%

Rationality over fashion and hype in drug design

et al. 2021

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“…This is particularly relevant with the advent of big data. Indeed, high-throughput experimental and virtual screening of large chemical databases generates an enormous amount of data that need to be stored and made accessible to convert data into information and, finally, to knowledge [ 14 ]. One of the applications of chemoinformatics (also known as chem(i)oinformatics) in NP-research is the organization, analysis, and dissemination of chemical information of NP in compound databases [ 11 , 12 ].…”

Section: Natural Product Databasesmentioning

confidence: 99%

Cheminformatics to Characterize Pharmacologically Active Natural Products

Medina-Franco

Saldívar-González

2020

Biomolecules

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Natural products have a significant role in drug discovery. Natural products have distinctive chemical structures that have contributed to identifying and developing drugs for different therapeutic areas. Moreover, natural products are significant sources of inspiration or starting points to develop new therapeutic agents. Natural products such as peptides and macrocycles, and other compounds with unique features represent attractive sources to address complex diseases. Computational approaches that use chemoinformatics and molecular modeling methods contribute to speed up natural product-based drug discovery. Several research groups have recently used computational methodologies to organize data, interpret results, generate and test hypotheses, filter large chemical databases before the experimental screening, and design experiments. This review discusses a broad range of chemoinformatics applications to support natural product-based drug discovery. We emphasize profiling natural product data sets in terms of diversity; complexity; acid/base; absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties; and fragment analysis. Novel techniques for the visual representation of the chemical space are also discussed.

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“…As commented hereunder, the notion of chemical space is also related to computational chemogenomics, where one aims to predict (and then validate experimentally) the intersection between the chemical and biologically relevant space. Indeed, in the early '60 s, the quantitative analysis of the SAR marked a significant milestone in the history of chemoinformatics and computer-aided drug design [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Progress on open chemoinformatic tools for expanding and exploring the chemical space

Medina-Franco

Sánchez-Cruz

López-López

et al. 2021

J Comput Aided Mol Des

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The concept of chemical space is a cornerstone in chemoinformatics, and it has broad conceptual and practical applicability in many areas of chemistry, including drug design and discovery. One of the most considerable impacts is in the study of structure–property relationships where the property can be a biological activity or any other characteristic of interest to a particular chemistry discipline. The chemical space is highly dependent on the molecular representation that is also a cornerstone concept in computational chemistry. Herein, we discuss the recent progress on chemoinformatic tools developed to expand and characterize the chemical space of compound data sets using different types of molecular representations, generate visual representations of such spaces, and explore structure–property relationships in the context of chemical spaces. We emphasize the development of methods and freely available tools focusing on drug discovery applications. We also comment on the general advantages and shortcomings of using freely available and easy-to-use tools and discuss the value of using such open resources for research, education, and scientific dissemination.

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Chemistry in Times of Artificial Intelligence

Cited by 68 publications

References 66 publications

Rationality over fashion and hype in drug design

Rationality over fashion and hype in drug design

Cheminformatics to Characterize Pharmacologically Active Natural Products

Progress on open chemoinformatic tools for expanding and exploring the chemical space

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