Ping Xiong, Simon Ming‐Yuen Lee, Ging Chan
Frontiers in Cardiovascular Medicine·
2022·
cited by 104
Medicine & Health
ECG Monitoring and Analysis
ai assisted
importance 8/10
scale stated
Early detection and localization of myocardial infarction via ECG is life-saving, but population-wide ECG screening is too labor-intensive without automation.
Nada M. Elshennawy, Dina M. Ibrahim
Diagnostics·
2020·
cited by 203
Medicine & Health
COVID-19 diagnosis using AI
partially solved
importance 8/10
scale stated
Detecting and classifying pneumonia from chest X-ray images with a model that meets all performance metrics.
Nikoletta-Maria Koutroumpa, Konstantinos D. Papavasileiou, Anastasios G. Papadiamantis, Georgia Melagraki, Antreas Afantitis
International Journal of Molecular Sciences·
2023·
cited by 35
Medicine & Health
Computational Drug Discovery Methods
ai assisted
importance 8/10
scale stated
Drug discovery is long and costly, and deep learning results are often not confirmed experimentally before impacting development.
Peng Zhang, Bin Chen, Liang Ma, Zhen Li, Zhichao Song, Wei Duan, Xiaogang Qiu
Computational Intelligence and Neuroscience·
2015·
cited by 26
Medicine & Health
Viral Infections and Outbreaks Research
ai assisted
importance 8/10
scale stated
Governments struggle to predict possible Ebola epidemic scenarios in practice when drawing up emergency plans.
Tiago Alves de Oliveira, Michel Pires da Silva, Eduardo Habib Bechelane Maia, Alisson Marques da Silva, Alex Gutterres Taranto
Drugs and Drug Candidates·
2023·
cited by 136
Medicine & Health
Computational Drug Discovery Methods
ai assisted
importance 8/10
scale stated
Drug discovery and repositioning demand high resource investment and are time-consuming for the pharmaceutical industry.
Stephen Rettie, David Juergens, Victor Adebomi, Yensi Flores Bueso, Qinqin Zhao, Alexandria N. Leveille, Andi Liu, Asim K. Bera et al.
Nature Chemical Biology·
2025·
cited by 75
Designing macrocyclic binders to therapeutic proteins relies on resource-intensive screening with little control over binding mode, and no robust de novo design approaches existed.
Justas Dauparas, Gyu Rie Lee, Robert J. Pecoraro, Linna An, Ivan V. Anishchenko, Cameron J. Glasscock, David A. Baker
Nature Methods·
2025·
cited by 278
Designing protein sequences that correctly interact with small molecules, nucleotides and metals — essential for enzymes, binders and sensors — which prior deep-learning methods could not model.
Jun Cheng, Guido Novati, Joshua Pan, Clare Bycroft, Akvilė Žemgulytė, Taylor Applebaum, Alexander Pritzel, Lai Hong Wong et al.
Science·
2023·
cited by 2,095
Medicine & Health
Genomics and Rare Diseases
partially solved
importance 7/10
scale stated
The vast majority of human missense genetic variants have unknown clinical significance, hindering genetic disease diagnosis.
Patrick Bryant, Gabriele Pozzati, Arne Elofsson
Nature Communications·
2022·
cited by 1,010
Predicting the structures of heterodimeric protein complexes, fundamental for understanding protein function, was unreliable with existing methods.
Babak Alipanahi, Andrew Delong, Matthew T. Weirauch, Brendan J. Frey
Nature Biotechnology·
2015·
cited by 2,869
Predicting which DNA and RNA sequences are bound by proteins is needed to model gene regulation and identify causal disease variants.
Matthew James Robson, Shengjun Xu, Zilong Wang, Qing Chen, Francesco Ciucci
Advanced Materials·
2025·
cited by 29
Discovering new aqueous deep eutectic electrolytes for zinc-ion batteries is time-consuming and resource-intensive.
Wei Hui Zheng, Qiqige Wuyun, Lydia Freddolino, Yang Zhang
Proteins Structure Function and Bioinformatics·
2023·
cited by 58
Predicting 3D structures of proteins and complexes, especially hard free-modeling targets, remains difficult.
Nadav Brandes, Grant Goldman, Charlotte H. Wang, Chun Ye, Vasilis Ntranos
Nature Genetics·
2023·
cited by 421
Existing deep-learning methods could not predict the effects of all possible human coding variants, limiting genetic diagnosis and interpretation.
Nathaniel R. Bennett, Brian Coventry, Inna Goreshnik, Buwei Huang, Aza Allen, Dionne K. Vafeados, Ying Po Peng, Justas Dauparas et al.
Nature Communications·
2023·
cited by 396
De novo design of high-affinity protein binders from target structure alone had a low overall success rate.
Andy Hsien-Wei Yeh, Christoffer Norn, Yakov Kipnis, Doug Tischer, Samuel J. Pellock, Declan Evans, Pengchen Ma, Gyu Rie Lee et al.
Nature·
2023·
cited by 441
Designing entirely new enzymes from scratch has been limited by a lack of suitable protein structures and the complexity of protein sequence-structure relationships.
A. S. M. Zisanur Rahman, Chengyou Liu, Hunter Sturm, Andrew M. Hogan, Rebecca L. Davis, Pingzhao Hu, Silvia T. Cardona
PLoS Computational Biology·
2022·
cited by 47
Medicine & Health
Computational Drug Discovery Methods
partially solved
importance 7/10
scale stated
Screening small molecule libraries for novel antibacterial compounds has a very low success rate (0.87% hit rate against antibiotic-resistant Burkholderia cenocepacia).
Philippe C. Wouters, Rutger R van de Leur, Melle B. Vessies, Antonius M W van Stipdonk, Mohammed Ali Ghossein, Rutger J. Hassink, Pieter A. Doevendans, Pim van der Harst et al.
European Heart Journal·
2022·
cited by 57
Medicine & Health
Cardiac pacing and defibrillation studies
partially solved
importance 7/10
scale stated
Current guideline ECG criteria poorly predict which patients will benefit from cardiac resynchronization therapy (CRT).
Feiran Li, Le Yuan, Hongzhong Lu, Gang Li, Yu Chen, Martin K. M. Engqvist, Eduard Johannes Kerkhoven, JENS KVIST NIELSEN
Nature Catalysis·
2022·
cited by 460
Experimentally measured enzyme turnover numbers (kcat) are sparse and noisy, limiting genome-scale metabolic modeling across organisms.
Jian Liu, Tianqi Wu, Zhiye Guo, Jie Hou, Jianlin Jack Cheng
Proteins Structure Function and Bioinformatics·
2021·
cited by 31
Predicting protein 3D structure from sequence, especially for template-free (ab initio) targets, remains difficult.
Ling‐Ping Cen, Jie Ji, Jianwei Lin, Si-Tong Ju, Hong-Jie Lin, Tai-Ping Li, Yun Wang, Jian-Feng Yang et al.
Nature Communications·
2021·
cited by 346
Detecting multiple retinal fundus diseases and conditions early enough to prevent irreversible visual impairment, previously addressed only one disease at a time.
Ittai Dayan, Holger R. Roth, Aoxiao Zhong, Ahmed Harouni, Amilcare Gentili, Anas Zainul Abidin, Andy Liu, Anthony Beardsworth Costa et al.
Nature Medicine·
2021·
cited by 823
Predicting future oxygen needs of symptomatic COVID-19 patients using data from many hospitals without sharing patient data.
Mingyu Chen, Bin Zhang, Win Topatana, Jiasheng Cao, He-pan Zhu, Sarun Juengpanich, Qijiang Mao, Hong Yu et al.
npj Precision Oncology·
2020·
cited by 244
Grading hepatocellular carcinoma requires visual inspection by experienced pathologists, and mutated gene status normally requires genomic testing.
Wei Hui Zheng, Yang Li, Chengxin Zhang, Robin Pearce, S. M. Mortuza, Yang Zhang
Proteins Structure Function and Bioinformatics·
2019·
cited by 208
Predicting 3D protein structures for targets that lack homologous templates, where conventional pipelines performed poorly.
Paul Baireuther, Thomas E. O’Brien, Brian Tarasinski, C. W. J. Beenakker
Quantum·
2018·
cited by 141
Physics & Astronomy
Quantum Computing Algorithms and Architecture
ai assisted
importance 7/10
scale stated
Fault-tolerant quantum computation requires efficient detection and correction of errors in encoded quantum information, and standard decoders miss correlations between bit-flip and phase-flip errors.