Table 1.

A summary of publications regarding the application of AI in improving immunotherapy, both in terms of efficacy prediction (based on structure and properties) and response prediction.

Medical Field	Identifying Novel Neoantigens
Time	Biomarker	Methods	Source	Outcome	Models
From 2016 to 2021	MHC peptide I and II class	Deep learning; recurrent neural network; neural network; convolutional neural network; machine learning; natural language processing	Mass spectrometry datasets; Immune Epitope Database (IEDB); SYFPEITHI database; RNA-seq data	Improving epitope–MHC interactions; MHC stability; immunogenicity; TCR binding; prediction of paired α/β TCR	(18 distinct studies) model EDGE, DeepHLApan, NMER, and NetMHC-4.0; NetMHCpan-4.0; MHCflurry; MHCflurry-2.0; Neonmhc2; Neopesee; pMTnet; ForestMHC; PRIME; MARIA; MHCSeqNet; HLAthena; NetTCR-2.0; NetMHCPan-4.1; NetMHCIIpan-4.0 [18]
Medical Field	Designing Antibodies
Time	Target	Methods	Source	Outcome	Models
From 2019 to 2022	CDRH3 regions and trastuzumab; CTLA-4 and PD-1 Abs; emibetuzumab.	Deep learning; convolutional neural network; long short-term memory;	CDR-H3 sequences public datasets	Improving target binding	(5 distinct studies, only 1 has a name) Ens-Grad [35,36,37,38,39]
	CDRH3 regions; CDR loops; or VH domains of antibodies.	Convolutional neural network; deep residual learning; ReNet	CDR-H3 sequences	Antibody structure prediction	(5 distinct studies) DeepH3; DeepAb; DeepSCAb; ABlooper; NanoNet [40,41,42,43,44]
	Abs solubility; viscosity.	Bi-LSTM network; RoBERTa; convolutional neural network; random forest classifier	Antibody sequences from repertoire sequencing	Forecasting pharmaceutical properties	(5 distinct studies) AbLSTM; BioPhi; solPredict; DeepSCM (used in two independent publications) [45,46,47,48,49]
Medical Field	Predicting Immunotherapy Effects
Time	Target	Methods	Outcome	Source	Cohort
From 2018 to 2022	Colorectal cancer and stomach cancer	Convolutional neural network; deep residual learning	Prediction of microsatellite instability	H&E histology from tissue banks	n = 94 whole-slide images from n = 81 patients [69,70]
	Colorectal cancer	ShuffleNet; MobileNetV2	Prediction of defective DNA mismatch repair and microsatellite instability	H&E histology from MSIDETECT consortium study	n = 8836 colorectal tumors (of all stages) [71]
	Cutaneous melanoma	Random forest classifier	Expression level of PD-L1 for precision of PD-L1 scoring	H&E histology	n = 69 cutaneous melanomas [55]
	Non-small-cell lung cancer	Deep learning	Tumor prediction score of PD-L1 expression	Whole-slide images	n = 173 IHC assay by using 22C3 binding Ab [61]
	Colorectal cancer	Deep learning	Prediction of tumor mutational burden	Histopathological images	n = 631 CRC patients in TCGA [59]
	24 cancer types	Machine learning	Prediction of cell composition in TME	Bulk RNA-seq	n = 9404 RNA-seq samples [79]
	13 cancer types	Convolutional neural network	Prediction of spatial cell composition in TME	H&E images	n = 4759 TCGA subjects [80]
	Breast cancer	Deep residual learning	Prediction of cell composition in TME	H&E image	n = 64 patients [81]
	Lung adenocarcinoma	Convolutional neural network	Prediction of spatial cell composition in TME	Spatial transcriptomic data and H&E images	n = 21 H&E images [82]
	Melanoma, gastric cancer, and bladder cancer	Machine learning	Prediction of immunotherapy response	Transcriptomic data	n = 91 melanoma; n = 45 gastric cancer; n = 348 bladder cancer [86]
	29 cancer types	MultiModal network		RNA-seq; genomic data	n = 8646 The Cancer Genome Atlas samples [69]
	Non-small-cell lung cancer	Multiple-instance LR		Histopathological images; genomic data	n = 247 patients [87]
	16 cancer types	Random forest	Prediction of Abs inhibitor response	Genomic and molecular data	n = 1479 patients [88]