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DOIs for this ISSN

Showing the top 5 of 126 DOIs, ordered by correction priority.

#	Title	Missing	Priority	Citations
1	Advances in Artificial Intelligence (AI)-assisted approaches… (10.1016/j.aichem.2023.100039)	Abstract ORCID	103.22	115
2	Machine learning advancements in organic synthesis: A focuse… (10.1016/j.aichem.2024.100049)	Abstract ORCID	92.25	69
3	Large-language models: The game-changers for materials scien… (10.1016/j.aichem.2024.100076)	Abstract ORCID	84.06	47
4	Molecular similarity: Theory, applications, and perspectives (10.1016/j.aichem.2024.100077)	Abstract ORCID	83.60	46
5	Top 20 influential AI-based technologies in chemistry (10.1016/j.aichem.2024.100075)	Abstract ORCID	79.55	38
6	Machine learning small molecule properties in drug discovery (10.1016/j.aichem.2023.100020)	Abstract ORCID	77.82	35
7	Automated Intelligent Platforms for High‐Throughput Chemical… (10.1016/j.aichem.2024.100057)	Abstract ORCID	77.82	35
8	Drug discovery and development in the era of artificial inte… (10.1016/j.aichem.2024.100070)	Abstract ORCID	75.26	31
9	Building a DFT+U machine learning interatomic potential for… (10.1016/j.aichem.2023.100042)	Abstract ORCID	65.05	19
10	Advances in machine-learning approaches to RNA-targeted drug… (10.1016/j.aichem.2024.100053)	Abstract ORCID	65.05	19
11	Size dependent lithium-ion conductivity of solid electrolyte… (10.1016/j.aichem.2024.100051)	Abstract ORCID	63.94	18
12	Recent advances of machine learning applications in the deve… (10.1016/j.aichem.2024.100068)	Abstract ORCID	62.76	17
13	Orders of coupling representations as a versatile framework… (10.1016/j.aichem.2023.100008)	Abstract ORCID	60.21	15
14	Intelligent vision for the detection of chemistry glassware… (10.1016/j.aichem.2023.100016)	Abstract ORCID	60.21	15
15	Metaheuristic optimisation of Gaussian process regression mo… (10.1016/j.aichem.2023.100021)	Abstract ORCID	58.80	14
16	Integrating machine learning with electrochemical sensors fo… (10.1016/j.aichem.2025.100105)	Abstract ORCID	57.31	13
17	AI's role in pharmaceuticals: Assisting drug design from pro… (10.1016/j.aichem.2023.100038)	Abstract ORCID	55.70	12
18	Comparison of dimensionality reduction techniques for the vi… (10.1016/j.aichem.2024.100055)	Abstract ORCID	55.70	12
19	Applying graph neural network models to molecular property p… (10.1016/j.aichem.2024.100050)	Abstract ORCID	53.96	11
20	Application of artificial intelligence and machine learning… (10.1016/j.aichem.2023.100011)	Abstract	52.34	123
21	Orders-of-coupling representation achieved with a single neu… (10.1016/j.aichem.2023.100013)	Abstract ORCID	52.07	10
22	Unveiling the impact of axial ligands on Fe-N-C complexes th… (10.1016/j.aichem.2023.100041)	Abstract ORCID	52.07	10
23	Machine learning insights into catalyst composition and stru… (10.1016/j.aichem.2024.100062)	Abstract ORCID	50.00	9
24	Machine learning and robot-assisted synthesis of diverse gol… (10.1016/j.aichem.2023.100028)	Abstract ORCID	47.71	8
25	Machine-learning-based virtual screening and ligand docking… (10.1016/j.aichem.2023.100014)	Abstract ORCID	45.15	7
26	Machine-learning-driven simulations on microstructure, therm… (10.1016/j.aichem.2023.100027)	Abstract ORCID	45.15	7
27	A machine learning-based high-precision density functional m… (10.1016/j.aichem.2023.100037)	Abstract ORCID	45.15	7
28	User-friendly and industry-integrated AI for medicinal chemi… (10.1016/j.aichem.2024.100072)	Abstract	43.51	54
29	Discovery of novel CaMK-II inhibitor for the possible mitiga… (10.1016/j.aichem.2023.100009)	Abstract ORCID	42.25	6
30	An accurate full-dimensional interaction potential energy su… (10.1016/j.aichem.2023.100019)	Abstract ORCID	42.25	6
31	QuantumBound – Interactive protein generation with one-shot… (10.1016/j.aichem.2023.100030)	Abstract ORCID	42.25	6
32	Reaction condition- and functional group-specific knowledge… (10.1016/j.aichem.2023.100034)	Abstract ORCID	42.25	6
33	Machine learning models to predict ligand binding affinity f… (10.1016/j.aichem.2023.100040)	Abstract ORCID	42.25	6
34	Artificial intelligence for drug repurposing against infecti… (10.1016/j.aichem.2024.100071)	Abstract	42.03	47
35	Chemical space navigation by machine learning models for dis… (10.1016/j.aichem.2023.100012)	Abstract ORCID	38.91	5
36	An improved artificial neural network fit of the ab initio p… (10.1016/j.aichem.2023.100017)	Abstract ORCID	38.91	5
37	Combining state-of-the-art quantum chemistry and machine lea… (10.1016/j.aichem.2023.100036)	Abstract ORCID	38.91	5
38	Synchrotron radiation data-driven artificial intelligence ap… (10.1016/j.aichem.2024.100045)	Abstract	36.18	27
39	Balancing Wigner sampling and geometry interpolation for dee… (10.1016/j.aichem.2023.100018)	Abstract ORCID	34.95	4
40	Exploration of SAM-I riboswitch inhibitors: In-Silico discov… (10.1016/j.aichem.2024.100044)	Abstract ORCID	34.95	4
41	Emerging technologies for drug repurposing: Harnessing the p… (10.1016/j.aichem.2024.100060)	Abstract ORCID	34.95	4
42	Data-driven modelling of corrosion behaviour in coated porou… (10.1016/j.aichem.2025.100086)	Abstract	34.04	22
43	Machine learning software to learn negligible elements of th… (10.1016/j.aichem.2023.100025)	Abstract ORCID	30.10	3
44	Prediction of 19F NMR chemical shift by machine learning (10.1016/j.aichem.2024.100043)	Abstract	30.10	15
45	A general strategy for improving the performance of PINNs --… (10.1016/j.aichem.2024.100047)	Abstract ORCID	30.10	3
46	RedPred, a machine learning model for the prediction of redo… (10.1016/j.aichem.2024.100064)	Abstract ORCID	30.10	3
47	Spatial-temporal self-attention network based on bayesian op… (10.1016/j.aichem.2024.100067)	Abstract ORCID	30.10	3
48	Machine Learning (ML)-driven quantitative structure-pharmaco… (10.1016/j.aichem.2025.100093)	Abstract ORCID	30.10	3
49	ML meets MLn: Machine learning in ligand promoted homogeneou… (10.1016/j.aichem.2023.100006)	Abstract	29.40	14
50	Empowering research in chemistry and materials science throu… (10.1016/j.aichem.2023.100035)	Abstract	29.40	14