Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR

Nature Reviews Drug Discovery, Journal Year: 2023, Volume and Issue: 23(2), P. 141 - 155

Published: Dec. 8, 2023

Language: Английский

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A Review of Transition Metal Boride, Carbide, Pnictide, and Chalcogenide Water Oxidation Electrocatalysts

Chemical Reviews, Journal Year: 2023, Volume and Issue: 123(23), P. 12795 - 13208

Published: Nov. 15, 2023

Transition metal borides, carbides, pnictides, and chalcogenides (X-ides) have emerged as a class of materials for the oxygen evolution reaction (OER). Because their high earth abundance, electrical conductivity, OER performance, these electrocatalysts potential to enable practical application green energy conversion storage. Under potentials, X-ide demonstrate various degrees oxidation resistance due differences in chemical composition, crystal structure, morphology. Depending on oxidation, catalysts will fall into one three post-OER electrocatalyst categories: fully oxidized oxide/(oxy)hydroxide material, partially core@shell unoxidized material. In past ten years (from 2013 2022), over 890 peer-reviewed research papers focused electrocatalysts. Previous review provided limited conclusions omitted significance "catalytically active sites/species/phases" this review, comprehensive summary (i) experimental parameters (e.g., substrates, loading amounts, geometric overpotentials, Tafel slopes, etc.) (ii) electrochemical stability tests post-analyses publications from 2022 is provided. Both mono polyanion X-ides are discussed classified with respect material transformation during OER. Special analytical techniques employed study reconstruction also evaluated. Additionally, future challenges questions yet be answered each section. This aims provide researchers toolkit approach showcase necessary avenues investigation.

Language: Английский

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Disordered enthalpy–entropy descriptor for high-entropy ceramics discovery

Nature, Journal Year: 2024, Volume and Issue: 625(7993), P. 66 - 73

Published: Jan. 3, 2024

The need for improved functionalities in extreme environments is fuelling interest high-entropy ceramics

Language: Английский

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AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: March 14, 2023

Closed-loop, autonomous experimentation enables accelerated and material-efficient exploration of large reaction spaces without the need for user intervention. However, advanced materials with complex, multi-step processes data sparse environments remains a challenge. In this work, we present AlphaFlow, self-driven fluidic lab capable discovery complex chemistries. AlphaFlow uses reinforcement learning integrated modular microdroplet reactor performing steps variable sequence, phase separation, washing, continuous in-situ spectral monitoring. To demonstrate power toward high dimensionality chemistries, use to discover optimize synthetic routes shell-growth core-shell semiconductor nanoparticles, inspired by colloidal atomic layer deposition (cALD). Without prior knowledge conventional cALD parameters, successfully identified optimized novel route, up 40 that outperformed sequences. Through capabilities closed-loop, learning-guided systems in exploring solving challenges nanoparticle syntheses, while relying solely on in-house generated from miniaturized microfluidic platform. Further application chemistries beyond can lead fundamental generation as well route discoveries optimization.

Language: Английский

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Self-driving laboratories to autonomously navigate the protein fitness landscape

Nature Chemical Engineering, Journal Year: 2024, Volume and Issue: 1(1), P. 97 - 107

Published: Jan. 11, 2024

Abstract Protein engineering has nearly limitless applications across chemistry, energy and medicine, but creating new proteins with improved or novel functions remains slow, labor-intensive inefficient. Here we present the Self-driving Autonomous Machines for Landscape Exploration (SAMPLE) platform fully autonomous protein engineering. SAMPLE is driven by an intelligent agent that learns sequence–function relationships, designs sends to a automated robotic system experimentally tests designed provides feedback improve agent’s understanding of system. We deploy four agents goal glycoside hydrolase enzymes enhanced thermal tolerance. Despite showing individual differences in their search behavior, all quickly converge on thermostable enzymes. laboratories automate accelerate scientific discovery process hold great potential fields synthetic biology.

Language: Английский

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Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back

Science, Journal Year: 2023, Volume and Issue: 382(6677)

Published: Dec. 21, 2023

A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties. We demonstrated two case studies on dye-like molecules, targeting absorption wavelength, lipophilicity, and photooxidative stability. In first study, experimentally realized 294 unreported across three automatic iterations design-make-test-analyze cycles while exploring structure-function space four rarely reported scaffolds. each iteration, property prediction models that guided exploration learned structure-property diverse scaffold derivatives, which were multistep syntheses a variety reactions. The second study exploited trained explored chemical previously discover nine top-performing within lightly space.

Language: Английский

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Self-Driving Laboratories for Chemistry and Materials Science

Chemical Reviews, Journal Year: 2024, Volume and Issue: 124(16), P. 9633 - 9732

Published: Aug. 13, 2024

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through automation experimental workflows, along with autonomous planning, SDLs hold potential to greatly accelerate research in chemistry and materials discovery. This review provides in-depth analysis state-of-the-art SDL technology, its applications across various disciplines, implications for industry. additionally overview enabling technologies SDLs, including their hardware, software, integration laboratory infrastructure. Most importantly, this explores diverse range domains where have made significant contributions, from drug discovery science genomics chemistry. We provide a comprehensive existing real-world examples different levels automation, challenges limitations associated each domain.

Language: Английский

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Single Atom Catalysts Based on Earth-Abundant Metals for Energy-Related Applications

Chemical Reviews, Journal Year: 2024, Volume and Issue: 124(21), P. 11767 - 11847

Published: July 5, 2024

Anthropogenic activities related to population growth, economic development, technological advances, and changes in lifestyle climate patterns result a continuous increase energy consumption. At the same time, rare metal elements frequently deployed as catalysts processes are not only costly view of their low natural abundance, but availability is often further limited due geopolitical reasons. Thus, electrochemical storage conversion with earth-abundant metals, mainly form single-atom (SACs), highly relevant timely technologies. In this review application SACs electrocatalytic chemicals fuels or products high content discussed. The oxygen reduction reaction also appraised, which primarily harnessed fuel cell technologies metal-air batteries. coordination, active sites, mechanistic aspects transition analyzed for two-electron four-electron pathways. Further, water splitting toward green hydrogen discussed terms evolution reaction. Similarly, production ammonia clean via nitrogen portrayed, highlighting potential single species.

Language: Английский

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In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science

Journal of the American Chemical Society, Journal Year: 2023, Volume and Issue: 145(40), P. 21699 - 21716

Published: Sept. 27, 2023

Exceptional molecules and materials with one or more extraordinary properties are both technologically valuable fundamentally interesting, because they often involve new physical phenomena compositions that defy expectations. Historically, exceptionality has been achieved through serendipity, but recently, machine learning (ML) automated experimentation have widely proposed to accelerate target identification synthesis planning. In this Perspective, we argue the data-driven methods commonly used today well-suited for optimization not realization of exceptional molecules. Finding such outliers should be possible using ML, only by shifting away from traditional ML approaches tweak composition, crystal structure, reaction pathway. We highlight case studies high-Tc oxide superconductors superhard demonstrate challenges ML-guided discovery discuss limitations automation task. then provide six recommendations development capable discovery: (i) Avoid tyranny middle focus on extrema; (ii) When data limited, qualitative predictions direction than interpolative accuracy; (iii) Sample what can made how make it defer optimization; (iv) Create room (and look) unexpected while pursuing your goal; (v) Try fill-in-the-blanks input output space; (vi) Do confuse human understanding model interpretability. conclude a description these integrated into workflows, which enable materials.

Language: Английский

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Delocalized, asynchronous, closed-loop discovery of organic laser emitters

Science, Journal Year: 2024, Volume and Issue: 384(6697)

Published: May 16, 2024

Contemporary materials discovery requires intricate sequences of synthesis, formulation, and characterization that often span multiple locations with specialized expertise or instrumentation. To accelerate these workflows, we present a cloud-based strategy enabled delocalized asynchronous design-make-test-analyze cycles. We showcased this approach through the exploration molecular gain for organic solid-state lasers as frontier application in optoelectronics. Distributed robotic synthesis in-line property characterization, orchestrated by artificial intelligence experiment planner, resulted 21 new state-of-the-art materials. Gram-scale ultimately allowed verification best-in-class stimulated emission thin-film device. Demonstrating integration five laboratories across globe, workflow provides blueprint delocalizing-and democratizing-scientific discovery.

Language: Английский

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