Machine Learning Methods for Small Data Challenges in Molecular Science

Chemical Reviews, Journal Year: 2023, Volume and Issue: 123(13), P. 8736 - 8780

Published: June 29, 2023

Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, technical limitations acquisition. However, big have been focus for past decade, small their challenges received little attention, even though they technically more severe machine learning (ML) deep (DL) studies. Overall, challenge is compounded by issues, diversity, imputation, noise, imbalance, high-dimensionality. Fortunately, current era characterized technological breakthroughs ML, DL, artificial intelligence (AI), which enable data-driven discovery, many advanced ML DL technologies developed inadvertently provided solutions problems. As a result, significant progress has made decade. In this review, we summarize analyze several emerging potential molecular science, including chemical biological sciences. We review both basic algorithms, linear regression, logistic regression (LR),

Language: Английский

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SELFIES and the future of molecular string representations

Patterns, Journal Year: 2022, Volume and Issue: 3(10), P. 100588 - 100588

Published: Oct. 1, 2022

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks chemistry materials science. Examples include the prediction of properties, discovery new reaction pathways, or design molecules. The needs read write fluently a chemical language each these tasks. Strings common tool represent molecular graphs, most popular string representation, Smiles, has powered cheminformatics since late 1980s. However, context AI ML chemistry, Smiles several shortcomings—most pertinently, combinations symbols lead invalid results with no valid interpretation. To overcome this issue, molecules was introduced 2020 that guarantees 100% robustness: SELF-referencing embedded (Selfies). Selfies simplified enabled numerous chemistry. In perspective, we look future discuss representations, along their respective opportunities challenges. We propose 16 concrete projects robust representations. These involve extension toward domains, exciting questions at interface languages, interpretability both humans machines. hope proposals will inspire follow-up works exploiting full potential representations

Language: Английский

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Crop Yield Prediction using Machine Learning and Deep Learning Techniques

Procedia Computer Science, Journal Year: 2023, Volume and Issue: 218, P. 406 - 417

Published: Jan. 1, 2023

Agriculture is a significant contributor to India's economic growth. The rising population of country and constantly changing climatic conditions have an impact on crop production food security. A variety factors influence selection, including market price, rate, soil type, rainfall, temperature, government policies, etc. Many changes are required in the agricultural sector order enhance Indian economy. In this research work authors implemented various machine learning techniques estimate yield Rajasthan state India five identified crops. results indicate that among all applied algorithms; Random Forest, SVM, Gradient Descent, long short-term memory, Lasso regression techniques; random forest performed better than others with 0.963 R2, 0.035 RMSE, 0.0251 MAE. were validated using root mean squared error, absolute error cross-validation techniques. This paper intends put selection method into practice help farmers solve problems.

Language: Английский

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Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

Nano-Micro Letters, Journal Year: 2023, Volume and Issue: 15(1)

Published: Oct. 13, 2023

Abstract Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method producing advanced is not only cost-ineffective but also time-consuming labor-intensive. Fortunately, advancement of machine learning brings new opportunities discovery design. By analyzing experimental theoretical data, can effectively predict their evolution reaction (HER) performance. This review summarizes recent developments in low-dimensional electrocatalysts, including zero-dimension nanoparticles nanoclusters, one-dimensional nanotubes nanowires, two-dimensional nanosheets, as well other electrocatalysts. In particular, effects descriptors algorithms on screening investigating HER performance highlighted. Finally, future directions perspectives electrocatalysis discussed, emphasizing potential to accelerate electrocatalyst discovery, optimize performance, provide insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding current state its research.

Language: Английский

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Accelerated Chemical Reaction Optimization Using Multi-Task Learning

ACS Central Science, Journal Year: 2023, Volume and Issue: 9(5), P. 957 - 968

Published: April 13, 2023

Functionalization of C-H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations require execution the presence polar functionality necessary protein binding. Recent work has shown effectiveness Bayesian optimization (BO) self-optimization chemical reactions; however, all previous cases these algorithmic procedures have started with no prior information about reaction interest. In this work, we explore use multitask (MTBO) several silico case studies by leveraging data collected from historical campaigns to accelerate new reactions. This methodology was then translated real-world, chemistry applications yield pharmaceutical intermediates using an autonomous flow-based reactor platform. The MTBO algorithm be successful determining optimal conditions unseen experimental activation reactions differing substrates, demonstrating efficient strategy large potential cost reductions when compared industry-standard process techniques. Our findings highlight as enabling tool workflows, representing step-change utilization and machine learning goal accelerated optimization.

Language: Английский

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Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy

Small Methods, Journal Year: 2023, Volume and Issue: 8(1)

Published: Oct. 27, 2023

Abstract Surface‐enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in broad range of fields including biomedicine, environmental protection, food safety among the others. In endless pursuit ever‐sensitive, robust, comprehensive sensing imaging, advancements keep emerging whole pipeline SERS, from design SERS substrates reporter molecules, synthetic route planning, instrument refinement, to data preprocessing analysis methods. Artificial intelligence (AI), which is created imitate eventually exceed human behaviors, exhibited its power learning high‐level representations recognizing complicated patterns with exceptional automaticity. Therefore, facing up intertwining influential factors explosive size, AI been increasingly leveraged all above‐mentioned aspects presenting elite efficiency accelerating systematic optimization deepening understanding about fundamental physics spectral data, far transcends labors conventional computations. this review, recent progresses are summarized through integration AI, new insights challenges perspectives provided aim better gear toward fast track.

Language: Английский

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Embracing data science in catalysis research

Nature Catalysis, Journal Year: 2024, Volume and Issue: 7(6), P. 624 - 635

Published: April 23, 2024

Language: Английский

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Toward an AI Era: Advances in Electronic Skins

Chemical Reviews, Journal Year: 2024, Volume and Issue: 124(17), P. 9899 - 9948

Published: Aug. 28, 2024

Electronic skins (e-skins) have seen intense research and rapid development in the past two decades. To mimic capabilities of human skin, a multitude flexible/stretchable sensors that detect physiological environmental signals been designed integrated into functional systems. Recently, researchers increasingly deployed machine learning other artificial intelligence (AI) technologies to neural system for processing analysis sensory data collected by e-skins. Integrating AI has potential enable advanced applications robotics, healthcare, human–machine interfaces but also presents challenges such as diversity model robustness. In this review, we first summarize functions features e-skins, followed feature extraction different models. Next, discuss utilization design e-skin address key topic implementation e-skins accomplish range tasks. Subsequently, explore hardware-layer in-skin before concluding with an opportunities various aspects AI-enabled

Language: Английский

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Guiding the rational design of biocompatible metal-organic frameworks for drug delivery

Matter, Journal Year: 2025, Volume and Issue: 8(3), P. 101958 - 101958

Published: Jan. 29, 2025

Language: Английский

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Emerging Artificial Intelligence (AI) Technologies Used in the Development of Solid Dosage Forms

Pharmaceutics, Journal Year: 2022, Volume and Issue: 14(11), P. 2257 - 2257

Published: Oct. 22, 2022

Artificial Intelligence (AI)-based formulation development is a promising approach for facilitating the drug product process. AI versatile tool that contains multiple algorithms can be applied in various circumstances. Solid dosage forms, represented by tablets, capsules, powder, granules, etc., are among most widely used administration methods. During process, factors including critical material attributes (CMAs) and processing parameters affect properties, such as dissolution rates, physical chemical stabilities, particle size distribution, aerosol performance of dry powder. However, conventional trial-and-error inefficient, laborious, time-consuming. has been recently recognized an emerging cutting-edge pharmaceutical which gained much attention. This review provides following insights: (1) general introduction sciences principal guidance from regulatory agencies, (2) approaches to generating database solid formulations, (3) insight on data preparation processing, (4) brief comparisons algorithms, (5) information applications case studies forms. In addition, powerful technique known deep learning-based image analytics will discussed along with its applications. By applying technology, scientists researchers better understand predict properties formulations facilitate more efficient processes.

Language: Английский

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