Results 221 to 230 of about 30,442 (292)

AI‐Driven Synthesis in Medicinal Chemistry: Integrating Large Language Models, Robotic Automation, and Sustainability Metrics to Accelerate Drug Discovery

Medicinal Research Reviews, EarlyView.
ABSTRACT Artificial intelligence (AI) is transforming synthetic chemistry from task‐specific predictors into integrated platforms that unify retrosynthesis, reaction optimization, and closed‐loop robotic automation. This review highlights how AI‐assisted planning and robotic execution shorten cycle times, reduce step counts, and improve route ...
Amit Gangwal, Antonio Lavecchia
wiley   +1 more source

Autoencoders for genomic variation analysis. [PDF]

Genome Res
Geleta M, Montserrat DM, Giro-I-Nieto X, Ioannidis AG.   +3 more
europepmc   +1 more source

Artificial Intelligence‐Driven Natural Product Drug Discovery: From Computational Genome Mining to Clinical Translation

Medicinal Research Reviews, EarlyView.
ABSTRACT Natural products (NPs) have historically yielded numerous therapeutic agents, yet their integration into modern drug discovery has been constrained by chemical complexity, low abundance, laborious dereplication, and limited target annotation.
Antonio Lavecchia
wiley   +1 more source

Major Depressive Disorder Signatures: A Review of Artificial Intelligence (AI)-Powered Insights Into Gut Dysbiosis. [PDF]

Health Sci Rep
Keshvari NZ, Sarkhab SAMN, Shahmoradi T, Pourashory M, Esmaeili A, Saleki K, Rezaei N.   +6 more
europepmc   +1 more source

Physics‐Informed Generative Machine Learning for Designing Crack‐Free γ′‐Strengthened Ni‐Based Superalloys for Laser Powder Bed Fusion

Materials Genome Engineering Advances, EarlyView.
This research proposes a physics‐informed generative machine learning framework to design SHA800, a crack‐free γ′‐strengthened nickel‐based superalloy for laser powder bed fusion, achieving a 43% γ′ volume fraction and 587 HV0.2 hardness. ABSTRACT Fabricating γ′‐strengthened nickel‐based superalloys via laser powder bed fusion (LPBF) faces significant ...
Kai Guo, Dongwei Guan, Tao Hu, Yaqing Hou, Xiaoqun Li, Zhongnan Bi, Songzhe Xu, Hao Yu, Wei Xu, Weidong Xuan, Chaoyue Chen, Zhongming Ren   +11 more
wiley   +1 more source

Limited-view signal recovery with frequency-aware denoising diffusion and geometry-integrated masked autoencoders for X-ray acoustic computed tomography. [PDF]

Photoacoustics
Peng J, Lu M, Cai B, Hu W, Zhou Q, Liu X.   +5 more
europepmc   +1 more source

Breaking Extrapolation Barriers: Dual Innovations for Property and Component Design of Multi‐Component Alloy

Materials Genome Engineering Advances, EarlyView.
This study breaks extrapolation barriers in alloy design by merging symbolic regression with latent space sampling. The dual‐strategy framework enables accurate prediction of high‐hardness properties and navigates uncharted compositional spaces. The approach successfully designs novel high‐entropy alloys with hardness exceeding 863.5 HV, demonstrating ...
Zhigang Yu, Boyu Zhao, Jianhua Chen, Jun Luan, Yunying Fan, Xingli Zou, Xionggang Lu   +6 more
wiley   +1 more source

Modeling visual memorability assessment with autoencoders reveals characteristics of memorable images. [PDF]

Sci Rep
Bagheri E, Mohsenzadeh Y.
europepmc   +1 more source

A Modular Hybrid Framework for CEST MRI Development and Clinical Translation Using pTx‐Pulseq Preparation With Open‐ and Closed‐Source Readouts and ONNX Mapping

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose Chemical exchange saturation transfer (CEST) MRI offers unique metabolic insights for imaging of cancer, stroke, or neurodegeneration. However, the rapid expansion of CEST preparation schemes and evaluation techniques creates a bottleneck; novel methods often remain complex prototypes requiring offline processing, preventing clinical ...
Martin Freudensprung, Junaid R. Rajput, Patrick Liebig, Simon Weinmüller, Ludwig Singer, Nikita Vladimirov, Or Perlman, Armin Michael Nagel, Arnd Doerfler, Moritz Zaiss   +9 more
wiley   +1 more source

Enhancing crayfish sex identification with Kolmogorov-Arnold networks and stacked autoencoders. [PDF]

Sci Rep
Atilkan Y, Kirik B, Acikbas ET, Ekinci F, Acici K, Asuroglu T, Benzer R, Guzel MS, Benzer S.   +8 more
europepmc   +1 more source