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Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure–activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.
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Urban Climate Informatics: An Emerging Research Field
The scientific field of urban climatology has long investigated the two-way interactions between cities and their overlying atmosphere through in-situ observations and climate simulations at various scales. Novel research directions now emerge through recent advancements in sensing and communication technologies, algorithms, and data sources. Coupled with rapid growth in computing power, those advancements augment traditional urban climate methods and provide unprecedented insights into urban atmospheric states and dynamics. The emerging field introduced and discussed here as Urban Climate Informatics (UCI) takes on a multidisciplinary approach to urban climate analyses by synthesizing two established domains: urban climate and climate informatics. UCI is a rapidly evolving field that takes advantage of four technological trends to answer contemporary climate challenges in cities: advances in sensors, improved digital infrastructure (e.g., cloud computing), novel data sources (e.g., crowdsourced or big data), and leading-edge analytical algorithms and platforms (e.g., machine learning, deep learning). This paper outlines the history and development of UCI, reviews recent technological and methodological advances, and highlights various applications that benefit from novel UCI methods and datasets.
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- Award ID(s):
- 1942805
- PAR ID:
- 10328203
- Date Published:
- Journal Name:
- Frontiers in Environmental Science
- Volume:
- 10
- ISSN:
- 2296-665X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fenvs.2022.867434
