Survey on recent progress of AI for chemistry: methods, applications, and opportunities
Hu DING , Pengxiang HUA , Zhen HUANG
Front. Comput. Sci. ›› 2026, Vol. 20 ›› Issue (11) : 2011358
Survey on recent progress of AI for chemistry: methods, applications, and opportunities
The development of artificial intelligence (AI) techniques has brought revolutionary changes across various realms. In particular, the use of AI-assisted methods to accelerate chemical research has become a popular and rapidly growing trend, leading to numerous groundbreaking works. In this paper, we provide a comprehensive review of current AI techniques in chemistry from a computational perspective, considering various aspects in the design of methods. We begin by discussing the characteristics of data from diverse sources, followed by an overview of various representation methods. Next, we review existing models for several topical tasks in the field, and conclude by highlighting some key challenges that warrant further attention.
artificial intelligence / machine learning / chemistry
| [1] |
|
| [2] |
|
| [3] |
|
| [4] |
|
| [5] |
|
| [6] |
|
| [7] |
|
| [8] |
|
| [9] |
|
| [10] |
|
| [11] |
|
| [12] |
|
| [13] |
|
| [14] |
|
| [15] |
|
| [16] |
|
| [17] |
|
| [18] |
|
| [19] |
|
| [20] |
|
| [21] |
|
| [22] |
|
| [23] |
|
| [24] |
|
| [25] |
|
| [26] |
|
| [27] |
|
| [28] |
|
| [29] |
|
| [30] |
|
| [31] |
|
| [32] |
|
| [33] |
|
| [34] |
|
| [35] |
|
| [36] |
|
| [37] |
|
| [38] |
|
| [39] |
|
| [40] |
|
| [41] |
|
| [42] |
|
| [43] |
|
| [44] |
|
| [45] |
|
| [46] |
|
| [47] |
Quantum machine learning platform. See quantum-machine.org/ website, 2024 |
| [48] |
|
| [49] |
|
| [50] |
|
| [51] |
|
| [52] |
|
| [53] |
|
| [54] |
|
| [55] |
|
| [56] |
|
| [57] |
|
| [58] |
|
| [59] |
|
| [60] |
|
| [61] |
|
| [62] |
|
| [63] |
|
| [64] |
|
| [65] |
|
| [66] |
|
| [67] |
|
| [68] |
|
| [69] |
|
| [70] |
|
| [71] |
|
| [72] |
|
| [73] |
|
| [74] |
|
| [75] |
|
| [76] |
|
| [77] |
|
| [78] |
|
| [79] |
|
| [80] |
|
| [81] |
|
| [82] |
|
| [83] |
|
| [84] |
|
| [85] |
|
| [86] |
|
| [87] |
|
| [88] |
|
| [89] |
|
| [90] |
|
| [91] |
|
| [92] |
|
| [93] |
|
| [94] |
|
| [95] |
|
| [96] |
|
| [97] |
|
| [98] |
|
| [99] |
|
| [100] |
Godwin J, Schaarschmidt M, Gaunt A L, Sanchez-Gonzalez A, Rubanova Y, Veličković P, Kirkpatrick J, Battaglia P. Simple GNN regularisation for 3D molecular property prediction and beyond. In: Proceedings of the 10th International Conference on International Conference on Learning Representations. 2022, 1−23 |
| [101] |
|
| [102] |
|
| [103] |
|
| [104] |
|
| [105] |
|
| [106] |
|
| [107] |
|
| [108] |
|
| [109] |
|
| [110] |
|
| [111] |
|
| [112] |
|
| [113] |
|
| [114] |
|
| [115] |
|
| [116] |
|
| [117] |
|
| [118] |
|
| [119] |
|
| [120] |
|
| [121] |
|
| [122] |
|
| [123] |
Rombach R, Blattmann A, Lorenz D, Esser P, Ommer B. High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 10684-10695. |
| [124] |
|
| [125] |
|
| [126] |
|
| [127] |
|
| [128] |
|
| [129] |
|
| [130] |
|
| [131] |
|
| [132] |
|
| [133] |
|
| [134] |
|
| [135] |
|
| [136] |
|
| [137] |
|
| [138] |
|
| [139] |
|
| [140] |
|
| [141] |
|
| [142] |
|
| [143] |
|
| [144] |
|
| [145] |
|
| [146] |
|
| [147] |
|
| [148] |
|
| [149] |
|
| [150] |
|
| [151] |
|
| [152] |
|
| [153] |
|
| [154] |
|
| [155] |
|
| [156] |
|
| [157] |
|
| [158] |
|
| [159] |
|
| [160] |
|
| [161] |
|
| [162] |
|
| [163] |
|
| [164] |
|
| [165] |
|
| [166] |
|
| [167] |
|
| [168] |
|
| [169] |
|
| [170] |
|
| [171] |
|
| [172] |
|
| [173] |
|
| [174] |
|
| [175] |
|
| [176] |
|
| [177] |
|
| [178] |
|
| [179] |
|
| [180] |
|
| [181] |
|
| [182] |
|
| [183] |
|
| [184] |
|
| [185] |
|
| [186] |
|
| [187] |
|
| [188] |
|
| [189] |
|
| [190] |
|
| [191] |
|
| [192] |
Mirza A, Alampara N, Kunchapu S, Emoekabu B, Krishnan A, Wilhelmi M, Okereke M, Eberhardt J, Elahi A M, Greiner M, Holick C T, Gupta T, Asgari M, Glaubitz C, Klepsch L C, Köster Y, Meyer J, Miret S, Hoffmann T, Kreth F A, Ringleb M, Roesner N, Schubert U S, Stafast L M, Wonanke A D D, Pieler M, Schwaller P, Jablonka K M. Are large language models superhuman chemists? 2024, arXiv preprint arXiv: 2404.01475 |
| [193] |
|
| [194] |
|
| [195] |
|
| [196] |
|
| [197] |
|
| [198] |
|
| [199] |
|
| [200] |
|
| [201] |
|
| [202] |
|
| [203] |
|
| [204] |
|
| [205] |
|
| [206] |
|
| [207] |
|
| [208] |
|
| [209] |
|
| [210] |
|
Higher Education Press
/
| 〈 |
|
〉 |