Robotics and automation provide potentially paradigm shifting improvements in the way materials are synthesized and characterized, generating large, complex data sets that are ideal for modeling and ...analysis by modern machine learning (ML) methods. Nanomaterials have not yet fully captured the benefits of automation, so lag behind in the application of ML methods of data analysis. Here, some key developments in, and roadblocks to the application of ML methods are reviewed to model and predict potentially adverse biological and environmental effects of nanomaterials. This work focuses on the diverse ways a range of ML algorithms are applied to understand and predict nanomaterials properties, provides examples of the application of traditional ML and deep learning methods to nanosafety, and provides context and future perspectives on developments that are likely to occur, or need to occur in the near future that allow artificial intelligence to make a deeper contribution to nanosafety.
Robotics and automation have accelerated synthesis and characterization of materials, providing large data sets for training machine learning models. Although nanomaterials have not fully exploited these technologies, here, key research on use of machine learning (ML) methods to predict potentially (eco)toxicological effects of nanomaterials is reviewed. Future perspectives on how artificial intelligence/ML can make deeper contributions to nanosafety are provided.
Materials science is undergoing a revolution, generating valuable new materials such as flexible solar panels, biomaterials and printable tissues, new catalysts, polymers, and porous materials with ...unprecedented properties. However, the number of potentially accessible materials is immense. Artificial evolutionary methods such as genetic algorithms, which explore large, complex search spaces very efficiently, can be applied to the identification and optimization of novel materials more rapidly than by physical experiments alone. Machine learning models can augment experimental measurements of materials fitness to accelerate identification of useful and novel materials in vast materials composition or property spaces. This review discusses the problems of large materials spaces, the types of evolutionary algorithms employed to identify or optimize materials, and how materials can be represented mathematically as genomes, describes fitness landscapes and mutation operators commonly employed in materials evolution, and provides a comprehensive summary of published research on the use of evolutionary methods to generate new catalysts, phosphors, and a range of other materials. The review identifies the potential for evolutionary methods to revolutionize a wide range of manufacturing, medical, and materials based industries.
Understanding the Roles of the "Two QSARs" Fujita, Toshio; Winkler, David A
Journal of chemical information and modeling,
02/2016, Letnik:
56, Številka:
2
Journal Article
Recenzirano
Quantitative structure-activity relationship (QSAR) modeling has matured over the past 50 years and has been very useful in discovering and optimizing drug leads. Although its roots were in ...extra-thermodynamic relationships within small sets of chemically similar molecules focused on mechanistic interpretation, a second class of QSAR models has emerged that relies on machine learning methods to generate models from large, chemically diverse data sets for predictive purposes. There has been a tension between the two groups of QSAR practitioners that is unnecessary and possibly counterproductive. This paper explains the difference in philosophy and application of these two distinct, but equally important, classes of QSAR models and how they can work together synergistically to accelerate the discovery of new drugs or materials.
Nanomaterials research is one of the fastest growing contemporary research areas. The unprecedented properties of these materials have meant that they are being incorporated into products very ...quickly. Regulatory agencies are concerned they cannot assess the potential hazards of these materials adequately, as data on the biological properties of nanomaterials are still relatively limited and expensive to acquire. Computational modelling methods have much to offer in helping understand the mechanisms by which toxicity may occur, and in predicting the likelihood of adverse biological impacts of materials not yet tested experimentally. This paper reviews the progress these methods, particularly those QSAR-based, have made in understanding and predicting potentially adverse biological effects of nanomaterials, and also the limitations and pitfalls of these methods.
•Nanomaterials regulators need good information to make good decisions.•Nanomaterials and their interactions with biology are very complex.•Computational methods use existing data to predict properties of new nanomaterials.•Statistical, data driven modelling methods have been successfully applied to this task.•Much more must be learnt before robust toolkits will be widely usable by regulators.
Ecologists studying bird foraging ecology have generally focused on food quantity over quality. Emerging work suggests that food quality, in terms of highly unsaturated omega‐3 fatty acids (HUFA), ...can have equally important effects on performance. HUFA, which are present in aquatic primary producers, are all but absent in vascular plants, and HUFA content is also correspondingly higher in aquatic insects. Here, we show that Tree Swallow (Tachycineta bicolor) chicks rapidly accumulate HUFA from food during the nestling period. Using data sampled over 24 years, we also show that Tree Swallow breeding success is positively associated with the availability of HUFA‐rich aquatic insects. Variation in aquatic insect biomass during chick development was a strong predictor of fledging success, whereas variation in terrestrial insects had little effect on fledging success. Our results highlight the potential for nutritional mismatches between insectivores and high‐quality prey to affect avian reproductive performance.
Abstract
The devastating impact of the COVID-19 pandemic caused by SARS–coronavirus 2 (SARS-CoV-2) has raised important questions about its origins and the mechanism of its transfer to humans. A ...further question was whether companion or commercial animals could act as SARS-CoV-2 vectors, with early data suggesting susceptibility is species specific. To better understand SARS-CoV-2 species susceptibility, we undertook an in silico structural homology modelling, protein–protein docking, and molecular dynamics simulation study of SARS-CoV-2 spike protein’s ability to bind angiotensin converting enzyme 2 (ACE2) from relevant species. Spike protein exhibited the highest binding to human (h)ACE2 of all the species tested, forming the highest number of hydrogen bonds with hACE2. Interestingly, pangolin ACE2 showed the next highest binding affinity despite having a relatively low sequence homology, whereas the affinity of monkey ACE2 was much lower despite its high sequence similarity to hACE2. These differences highlight the power of a structural versus a sequence-based approach to cross-species analyses. ACE2 species in the upper half of the predicted affinity range (monkey, hamster, dog, ferret, cat) have been shown to be permissive to SARS-CoV-2 infection, supporting a correlation between binding affinity and infection susceptibility. These findings show that the earliest known SARS-CoV-2 isolates were surprisingly well adapted to bind strongly to human ACE2, helping explain its efficient human to human respiratory transmission. This study highlights how in silico structural modelling methods can be used to rapidly generate information on novel viruses to help predict their behaviour and aid in countermeasure development.
The withdrawal of effective but toxic corrosion inhibitors has provided an impetus for the discovery of new, benign organic compounds to fill that role. Concurrently, developments in the ...high-throughput synthesis of organic compounds, the establishment of large libraries of available chemicals, accelerated corrosion inhibition testing technologies, and the increased capability of machine learning methods have made discovery of new corrosion inhibitors much faster and cheaper than it used to be. We summarize these technical developments in the corrosion inhibition field and describe how data-driven machine learning methods can generate models linking molecular properties to corrosion inhibition that can be used to predict the performance of materials not yet synthesized or tested. We briefly summarize the literature on quantitative structure–property relationships models of small organic molecule corrosion inhibitors. The success of these models provides a paradigm for rapid discovery of novel, effective corrosion inhibitors for a range of metals and alloys in diverse environments.
The statistical metrics used to characterize the external predictivity of a model, i.e., how well it predicts the properties of an independent test set, have proliferated over the past decade. This ...paper clarifies some apparent confusion over the use of the coefficient of determination, R(2), as a measure of model fit and predictive power in QSAR and QSPR modeling. R(2) (or r(2)) has been used in various contexts in the literature in conjunction with training and test data for both ordinary linear regression and regression through the origin as well as with linear and nonlinear regression models. We analyze the widely adopted model fit criteria suggested by Golbraikh and Tropsha ( J. Mol. Graphics Modell. 2002 , 20 , 269 - 276 ) in a strict statistical manner. Shortcomings in these criteria are identified, and a clearer and simpler alternative method to characterize model predictivity is provided. The intent is not to repeat the well-documented arguments for model validation using test data but rather to guide the application of R(2) as a model fit statistic. Examples are used to illustrate both correct and incorrect uses of R(2). Reporting the root-mean-square error or equivalent measures of dispersion, which are typically of more practical importance than R(2), is also encouraged, and important challenges in addressing the needs of different categories of users such as computational chemists, experimental scientists, and regulatory decision support specialists are outlined.
QSAR without borders Muratov, Eugene N; Bajorath, Jürgen; Sheridan, Robert P ...
Chemical Society reviews,
06/2020, Letnik:
49, Številka:
11
Journal Article
Recenzirano
Odprti dostop
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.
Nanoparticles are finding many applications in medicine and other fields like photonics. Magnetic nanoparticles have additional advantages in medicine over non-magnetic hard nanoparticles, as their ...magnetic properties make them ideal for hyperthermic applications in therapy and for sensitive diagnostic imaging applications. I review the literature on computational models of the magnetic properties of nanoparticles specifically. Such models have the potential to accelerate the design of magnetic nanoparticles for medical applications. Much of the current literature relates to the modelling of magnetic nanoparticles for inducing hyperthermia in aberrant cells, with significant bodies of work aimed at simulating and predicting properties for medical imaging and targeted delivery of drugs and gene therapies.
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