Massively multiplex chemical transcriptomics at single-cell resolution

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625

limma powers differential expression analyses for RNA-sequencing and microarray studies

Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458

BEDTools: a flexible suite of utilities for comparing genomic features

Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437

UMAP: Uniform Manifold Approximation and Projection

McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018) · Journal of Open Source Software · doi:10.21105/joss.00861 · Cited by 9,826

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al. (2012) · Nature · doi:10.1038/nature11003 · Cited by 7,950

The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells

Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101

Stochastic Gene Expression in a Single Cell

Elowitz, M. B., Levine, A. J., Siggia, E. D., & Swain, P. S. (2002) · Science · doi:10.1126/science.1070919 · Cited by 5,441

The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease

Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., Lerner, J., Brunet, J.-P., Subramanian, A., Ross, K. N., et al. (2006) · Science · doi:10.1126/science.1132939 · Cited by 5,257

Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq

Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al. (2016) · Science · doi:10.1126/science.aad0501 · Cited by 4,944

Massively multiplex multimodal chemical screens at single-cell resolutionPreprint

Chen, K. Y., Lopez, R., Eraslan, B., Hata, M., Takeshima, Y., Makino, K., Kibayashi, T., Ichiyama, K., Biton, A., Huetter, J.-C., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.23.727396 · Cited by 1

Unbalanced Perturbation Dynamics For Cell Fate DesignPreprint

Peng, Q., Wang, Y., Li, J., Wang, X., Xiao, Y., & Zhou, P. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.30.735555 · Cited by 1

A systematic comparison of single-cell perturbation response prediction models

Li, L., You, Y., Fu, Y., Liao, W., Fan, X., Lu, S., Cao, Y., Li, B., Ren, W., Kong, J., et al. (2026) · Science Advances · doi:10.1126/sciadv.aed3414 · Cited by 1

Establishing a transcriptome-based drug discovery paradigm for neurodevelopmental disordersPreprint

Dhindsa, R. S., Zoghbi, A. W., Krizay, D. K., Vasavda, C., & Goldstein, D. B. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.13.093468

Next Generation Sequencing Methods: Pushing the Boundaries

Finnegan, L. A., Crispie, F., Claesson, M. J., Cotter, P. D., & Kenny, J. G. (2020) · Elsevier eBooks · doi:10.1016/b978-0-08-100596-5.22688-7

Transcriptomic profiling of tissue environments critical for post-embryonic patterning and morphogenesis of zebrafish skinPreprint

Aman, A. J., Saunders, L. M., Carr, A. A., Srivatsan, S. R., Eberhard, C. D., Carrington, B., Watkins‐Chow, D. E., Pavan, W. J., Trapnell, C., & Parichy, D. M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.12.443782

The Effect of 1α (OH) D_3 and Interferon-α in a Case of Cutaneous T-cell Lymphoma

横手, 幸., 王, 伯., 森尾, 聡., 平沢, 晃., 比留間, 潔., 脇田, 久., 遠藤, 伸., 吉田, 尚., 青墳, 信., 浅井, 隆., et al. (1991) · Institutional Repositories DataBase (IRDB)

Interrogating the Impact of Δ9-Tetrahydrocannabinol Use During Adolescence: Microglia Lead the Way

Stella, N. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.09.006

An optimized approach for multiplexing single-nuclear ATAC-seq using oligonucleotide conjugated antibodiesPreprint

Bera, B. S., Thompson, T. V., Sosa, E., Nomaru, H., Reynolds, D. M., Dubin, R. A., Maqbool, S. B., Zheng, D., Morrow, B. E., Greally, J. M., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.22.521637

Will gazing into the single-cell multiome illuminate cardiovascular drug development?

Zhao, T. X., & Mallat, Z. (2023) · Nature Cardiovascular Research · doi:10.1038/s44161-022-00209-3

Supplementary Table from Developmental Deconvolution for Classification of Cancer Origin

Moiso, E., Farahani, A. A., Marble, H. D., Hendricks, A., Mildrum, S., Levine, S. S., Lennerz, J. K., & Garg, S. (2023) · doi:10.1158/2159-8290.22541776

Supplementary Table from Developmental Deconvolution for Classification of Cancer Origin

Moiso, E., Farahani, A. A., Marble, H. D., Hendricks, A., Mildrum, S., Levine, S. S., Lennerz, J. K., & Garg, S. (2023) · doi:10.1158/2159-8290.22541758.v1

Differentiation latency, cell division symmetry, and dormancy signatures define fetal liver HSCs at single cell resolutionPreprint

Ishida, T., Mercoli, J. I., Heck, A. M., Phelps, I. G., Varnum‐Finney, B., Dozono, S., Nourigat-Mckay, C., Kraskouskas, K., Wellington, R., Waltner, O. G., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.01.543314 · Cited by 1

Differentiation Latency and Dormancy Signatures Define Fetal Liver HSCs at Single Cell ResolutionPreprint

Ishida, T., Heck, A. M., Varnum‐Finney, B., Dozono, S., Nourigat-Mckay, C., Kraskouskas, K., Wellington, R., Waltner, O. G., Root, C., Jackson, D. L., et al. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4476677

Transcriptomic profiling of tissue environments critical for post-embryonic patterning and morphogenesis of zebrafish skinPreprint

Aman, A. J., Saunders, L. M., Carr, A. A., Srivatsan, S. R., Eberhard, C. D., Carrington, B., Watkins‐Chow, D. E., Pavan, W. J., Trapnell, C., & Parichy, D. M. (2023) · eLife · doi:10.7554/elife.86670.3

Precision clinical genomics and single subject studies of multi-omics data

Miyashita, H., Kato, S., & Sicklick, J. K. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-824010-6.00062-9

A multiplexed, single-cell sequencing screen identifies compounds that increase neurogenic reprogramming of murine Muller gliaPreprint

Tresenrider, A. M., Hooper, M. J., Todd, L. J., Kierney, F., Blasdel, N., Trapnell, C., & Reh, T. A. (2023) · eLife · doi:10.7554/elife.92091.1

A starting guide on multi-omic single-cell data joint analysis: basic practices and resultsPreprint

Martini, L., Bardini, R., & Carlo, S. D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.30.587427

LaGrACE: Estimating gene program dysregulation using latent gene regulatory network for biomedical discoveryPreprint

Jia, M., Mao, H., Zhou, M., Chen, Y., & Benos, P. V. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.29.591756

Genetic-to-Chemical Perturbation Transfer Learning Through Unified Multimodal Molecular RepresentationsPreprint

Li, Y., Zeng, M., Zhu, J., Liu, L., Wang, F., Huang, L.-K., Yang, F., Li, M., & Yao, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.02.635055

BADGER: biologically-aware interpretable differential gene expression ranking model

Kim, H., Gim, M., Baek, S., Park, S., Kim, S., & Kang, J. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbaf029

scDrugLink: Single-Cell Drug Repurposing for CNS Diseases via Computationally Linking Drug Targets and Perturbation Signatures

Huang, L., Lu, X., & Chen, D. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3552536

A review on single-cell perturbation data analysis

Fu, Z., Yang, M., & Hou, L. (2025) · Scientia Sinica Mathematica · doi:10.1360/ssm-2024-0315

Transfer learning using generative pretrained genomic DNA models for predicting perturbation-induced changes in gene expressionPreprint

Shihashi, T., & Nikaido, I. (2025) · Research Square · doi:10.21203/rs.3.rs-6461531/v1

Protocol for predicting single- and multiple-dose-dependent gene expression using deep generative learning

Bowman, D. E., Panda, V., Marri, D., Kana, O. Z., & Bhattacharya, S. (2025) · STAR Protocols · doi:10.1016/j.xpro.2025.103932

Scalable, fast and accurate differential gene expression testing from millions of cells of multiple patients

Santacatterina, G., Tosato, N., Milite, S., Davydzenka, K., Insaghi, E., Sanguinetti, G., Cozzini, S., Egidi, L., & Caravagna, G. (2026) · Nature Communications · doi:10.1038/s41467-026-74451-9

Systems biology methods to study the epigenome and metabolic-epigenetic interactions

Manjunatha, P. B., Bhowmick, R., & Chandrasekaran, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-26759-8.00006-0

CMDdemux: an efficient single cell demultiplexing method

Wang, J., Chen, L., Brown, D. V., Chiu, C. Y. H., & Speed, T. P. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag360

Advances in single-cell perturbation modeling

Qi, X., Zhao, L., Chen, R., & Zhao, Y. C. (2025) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/csb-2025-0682

Enhanced Multiplexed Single-Cell RNA-Sequencing for Accurate Detection of Treatment Effects Without Batch Correction in the Avian Embryonic ModelPreprint

Maupetit‐Mehouas, S., Maurelia, F., Hosseinna, P., Allègre, N., Renaud, Y., Cruzel, J., Chazaud, C., & Guillot, C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.30.679584

SCREAM: Single-cell Clustering using Representation Autoencoder of MultiomicsPreprint

Chrysinas, P., Venkatesan, S., Patel, P. G., & Gunawan, R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.03.680290

Inhibiting Cytosine-Adenine-Guanine (CAG) repeat expansions as a therapeutic strategy for Huntington’s disease

Winquist, R. J., & Church, B. (2025) · Biochemical Pharmacology · doi:10.1016/j.bcp.2025.117438

scDRP: disentangled representation learning for predicting single-cell responses to perturbations and estimating individual treatment effects

Sun, J., Stojanov, P., & Zhang, K. (2026) · Briefings in Bioinformatics · doi:10.1101/2025.11.21.689783

Drug Response Prediction Provides a Biologically Relevant Benchmark for Perturbation Response ModelsPreprint

Brouwer, N., Damyanov, M., Argelo, J., Vis, D. J., Reinders, M. J. T., & Wessels, L. F. A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.09.693213 · Cited by 1

VIP-OT: Dissecting Single-Cell Biochemical State Dynamics under Perturbation via Vibrational Painting and Optimal TransportPreprint

Li, X., Stark, S. G., Liu, X., Wei, M., Zhao, Z., Qi, J., Chen, X., Dang, T. N. S., Wu, Y., Zhang, K., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.09.693255

Additional file 2 of FIPRESCI: droplet microfluidics based combinatorial indexing for massive-scale 5′-end single-cell RNA sequencing

Li, Y., Huang, Z., Zhang, Z., Wang, Q., Li, F., Wang, S., Ji, X., Shu, S., Fang, X., & Jiang, L. (2024) · Figshare · doi:10.6084/m9.figshare.26580893

Additional file 2 of deMULTIplex2: robust sample demultiplexing for scRNA-seq

Zhu, Q., Conrad, D. N., & Gartner, Z. J. (2024) · Figshare · doi:10.6084/m9.figshare.25116179

Additional file 5 of FIPRESCI: droplet microfluidics based combinatorial indexing for massive-scale 5′-end single-cell RNA sequencing

Li, Y., Huang, Z., Zhang, Z., Wang, Q., Li, F., Wang, S., Ji, X., Shu, S., Fang, X., & Jiang, L. (2024) · Figshare · doi:10.6084/m9.figshare.26580902

Additional file 4 of FIPRESCI: droplet microfluidics based combinatorial indexing for massive-scale 5′-end single-cell RNA sequencing

Li, Y., Huang, Z., Zhang, Z., Wang, Q., Li, F., Wang, S., Ji, X., Shu, S., Fang, X., & Jiang, L. (2024) · Figshare · doi:10.6084/m9.figshare.26580899

Additional file 3 of FIPRESCI: droplet microfluidics based combinatorial indexing for massive-scale 5′-end single-cell RNA sequencing

Li, Y., Huang, Z., Zhang, Z., Wang, Q., Li, F., Wang, S., Ji, X., Shu, S., Fang, X., & Jiang, L. (2024) · Figshare · doi:10.6084/m9.figshare.26580896

Additional file 7 of FIPRESCI: droplet microfluidics based combinatorial indexing for massive-scale 5′-end single-cell RNA sequencing

Li, Y., Huang, Z., Zhang, Z., Wang, Q., Li, F., Wang, S., Ji, X., Shu, S., Fang, X., & Jiang, L. (2024) · Figshare · doi:10.6084/m9.figshare.26580908

Additional file 1 of MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks

Yu, H., & Welch, J. D. (2021) · Figshare · doi:10.6084/m9.figshare.14634020

Additional file 1 of FIPRESCI: droplet microfluidics based combinatorial indexing for massive-scale 5′-end single-cell RNA sequencing

Li, Y., Huang, Z., Zhang, Z., Wang, Q., Li, F., Wang, S., Ji, X., Shu, S., Fang, X., & Jiang, L. (2024) · Figshare · doi:10.6084/m9.figshare.26580890

Additional file 3 of deMULTIplex2: robust sample demultiplexing for scRNA-seq

Zhu, Q., Conrad, D. N., & Gartner, Z. J. (2024) · Figshare · doi:10.6084/m9.figshare.26674730

Additional file 2 of MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks

Yu, H., & Welch, J. D. (2021) · Figshare · doi:10.6084/m9.figshare.14634023

Additional file 6 of FIPRESCI: droplet microfluidics based combinatorial indexing for massive-scale 5′-end single-cell RNA sequencing

Li, Y., Huang, Z., Zhang, Z., Wang, Q., Li, F., Wang, S., Ji, X., Shu, S., Fang, X., & Jiang, L. (2024) · Figshare · doi:10.6084/m9.figshare.26580905

Additional file 1 of An optimized approach for multiplexing single-nuclear ATAC-seq using oligonucleotide-conjugated antibodies

Bera, B. S., Thompson, T. V., Sosa, E., Nomaru, H., Reynolds, D. M., Dubin, R., Maqbool, S. B., Zheng, D., Morrow, B. E., Greally, J. M., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22721513

Additional file 2 of An optimized approach for multiplexing single-nuclear ATAC-seq using oligonucleotide-conjugated antibodies

Bera, B. S., Thompson, T. V., Sosa, E., Nomaru, H., Reynolds, D. M., Dubin, R., Maqbool, S. B., Zheng, D., Morrow, B. E., Greally, J. M., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22721516

Additional file 1 of deMULTIplex2: robust sample demultiplexing for scRNA-seq

Zhu, Q., Conrad, D. N., & Gartner, Z. J. (2024) · Figshare · doi:10.6084/m9.figshare.25116023

Interpretably Predicting Chemical Perturbation via Biologically Informed Visible Neural Network

Zhou, W., Xie, J., & Yang, Y. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356611

Fine-Grained Cross-Attention Between Drug Structures and Genes for Perturbation Prediction

Lv, H., Bai, Y., & Luo, J. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356593

scCAVAE: Modeling Synergistic Interactions in Perturbation Responses via Attention and Hierarchical Supervised Contrastive Learning

Li, X., Zhan, B., Zhang, J., & Yan, S. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356521

IDCLP: A Deep Learning Framework for Predicting Chemical-Induced Gene Expression Profiles Through Multisource Data Integration

Mao, G., Yip, H. F., & Zhang, L. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356206

A modular transcript enrichment strategy for scalable, atlas-aligned, and clonotype-resolved single-cell transcriptomicsPreprint

Vaikunthan, M., Schoonen, A. C. M., Lia, I.-T., Guo, J., & McFaline‐Figueroa, J. L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.06.703342

PACMON: Pathway-guided Multi-Omics data integration for interpreting large-scale perturbation screensPreprint

Qoku, A., Stickel, T., AmeriFar, S., Oellerich, T., Wolf, S., & Buettner, F. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.20.713295

Global scientific growth and thematic development of spatial transcriptomics in oncology research

Sweileh, M. W. (2026) · Discover Oncology · doi:10.1007/s12672-026-04937-x

Étude de l’origine moléculaire de l’hétérogénéité de réponse cellulaire à TRAIL et son rôle dans la résistance non-génétique

Meyer, M. (2020) · theses.fr (ABES) · doi:10.70675/1a8e6d5bzc60fz4d57z9f05zbbc81aeec00a

Predicting condition-aware drug-induced transcriptional responses via a latent diffusion model

Kim, C., & Yoo, S. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag173 · Cited by 1

Deep generative framework for modeling single-cell drug perturbation response

Zhang, Y., Wu, C., Li, T., Zhou, Z., Huang, Z., Zhang, A., & Wang, Z. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.109005

HyperMap: An Efficient Framework for Transferring Perturbation Responses Across Diverse Biological ContextsPreprint

Dhaka, B., Gao, J., & Ideker, T. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.23.720505 · Cited by 1

Pathway-informed Universal Domain Adaptation for Single-cell RNA-seq DataPreprint

Wei, X., Li, X., Liu, H., Du, G., Wei, F., & Shang, X. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.07.723423

Combinatorial transcription factor interactions drive modular gene regulatory networksPreprint

Duan, J., Li, B., Kulkarni, K., Orquera-Tornakian, G., Barth, D., Wang, L. H., Pandit, V., Liou, J., Munshi, N. V., & Hon, G. C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.20.726505

Covalent Photo-Aptamer Tagging (CPAT) for Single-Cell Multiplexing and Chemical Transcriptomic Screens

Ma, X. C., Luo, G., Jia, H.-R., Tam, K. Y., Tan, W., & Wu, Q. (2026) · Analytical Chemistry · doi:10.1021/acs.analchem.6c02091

Quantitative mapping of heterochrony to species-specific phenotypesPreprint

Bourn, J. J., Knoblich, S., Kneeshaw, S., Benjaminsen, J., Žílová, L., Aulehla, A., Saunders, L. M., Wittbrodt, J., Birney, E., & Dorrity, M. W. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.15.732302

V3Cell: A Vision-Guided Virtual 3D Cell Framework for Phenotypic Modeling and Perturbation PredictionPreprint

Lu, Y., Xun, D., Xu, C., Zhu, X., Zhang, Z. D., Chen, P., Yang, X., Yan, Z., Rao, J., Huili, H., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.23.734130

Learning Perturbation Effects Through Contrastive Alignment of Multimodal Biological EmbeddingsPreprint

Long, W., Liu, T., Szałata, A., Theis, F. J., Xue, L., & Zhao, H. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.23.734145 · Cited by 1

Network Pharmacology Without Oversimplification: Designing a Mechanism-Centered Model for Polyherbal Formulations and Multi-Target Therapeutics

Zhang, W., Hui, C., & Tan, M. (2025) · International Journal of Pharmaceutical and Phytopharmacological Research · doi:10.51847/vtfecfftct

A Neural-Network-Free Calibration Matches or Beats Deep Single-Cell Perturbation Response Models Across Four Datasets

Sun, B., Zheng, H., Chen, J., & Jin, J. (2026) · Genes · doi:10.3390/genes17070816

CellTarNet: Robust Single-Cell Perturbation Prediction using Transformer Generative Model

Shankar, S. S. (2026) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3807503.3819992

Combinatorial transcription factor interactions drive modular gene regulatory networksPreprint

Duan, J., Li, B., Kulkarni, K., Orquera-Tornakian, G., Barth, D., Wang, L. H., Pandit, V., Liou, J., Munshi, N. V., & Hon, G. C. (2026) · eLife · doi:10.7554/elife.111977.1

Combinatorial transcription factor interactions drive modular gene regulatory networksPreprint

Duan, J., Li, B., Kulkarni, K., Orquera-Tornakian, G., Barth, D., Wang, L. H., Pandit, V., Liou, J., Munshi, N. V., & Hon, G. C. (2026) · eLife · doi:10.7554/elife.111977

Causal Target Validation Must Separate Molecular Association, Biological Mediation, Disease Modification, and Therapeutic Intervention

Chang, M., Martin, A.-S., Oduor, R., & Lehmann, T. (2025) · Pharmacophore · doi:10.51847/wwzoralyut

A leakage-aware benchmark for evaluating chemical and cellular generalization in single-cell perturbation-response predictionPreprint

Lin¹, D., Chen², Y., Liu², Y., & Zhang¹, Y. (2026) · Research Square · doi:10.21203/rs.3.rs-10446347/v1

World Models for Pharmaceutical Science Should Simulate Biological Consequences rather than Merely Continue Statistical Patterns

Zhang, M., Takahashi, D., Benali, Y., & Liu, X. (2025) · Pharmacophore · doi:10.51847/efvx6kqqna

Perturbation Atlases as Pharmacological Reasoning Engines for Connecting Molecular Interventions to Context-Dependent Cellular Responses

Kadiatou, D., Camara, F., KEITA, M., & Leclercq, S. (2025) · Pharmacophore · doi:10.51847/a1xh4xuw5o

Cell State, Not Cell Type, Should Anchor Computational Strategies for Therapeutic Target Discovery

Hamdy, A., Khalil, M., & Ibrahim, Y. (2025) · Pharmacophore · doi:10.51847/ebseqibybb

PerturbLDM: conditional latent diffusion for modelling single-cell perturbation responsesPreprint

Yu, L., Hsieh, K., Chu, Y., Lan, Q., Zhao, X., Hsu, Y.-C., Wood, C. S., Rasmy, L., Pilie, P. G., Zhi, D., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.07.743610

AI-Driven Drug–Target Interaction Prediction: From Data Representation to Model Design

Hu, J., Wu, L., He, S., Bo, X., & Lu, J. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c01920

A mechanism-annotated benchmark reveals limited fidelity to drug-response signatures in single-cell perturbation modelsPreprint

Li, L., Duan, S., Zha, X., Ye, F., Zhang, Y., Zhang, X., Cao, Y., Liu, C., & Fang, B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.19.745729

A knowledge-driven framework for predicting single-cell responses for unprofiled drugs

Feng, J., Zhao, Z., Zhang, X., Liu, M., Chen, J., Quan, X., Fu, B., Zhang, J., Wang, Y., Zhang, Y., et al. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01286-w · Cited by 1

A pretrained unified model enables cellular functional profile prediction and multi-objective virtual drug screeningPreprint

Chen, R., Huang, L., Qiao, Y., Mandal, S., Mo, L., Li, L., Leshchiner, D., Zhang, X., Pu, J., Xie, Y., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.25.746866

Data-centric feedback loops for next-generation immunotherapy development

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Single-cell mapping of homocysteine-induced perturbations in avian embryogenesis using LMO and BD rhapsody transcriptomics

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