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Cited by in F6Publishing

For:	Born J, Huynh T, Stroobants A, Cornell WD, Manica M. Active Site Sequence Representations of Human Kinases Outperform Full Sequence Representations for Affinity Prediction and Inhibitor Generation: 3D Effects in a 1D Model. J Chem Inf Model 2021. [PMID: 34905358 DOI: 10.1021/acs.jcim.1c00889] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]

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Born J, Huynh T, Stroobants A, Cornell WD, Manica M. Active Site Sequence Representations of Human Kinases Outperform Full Sequence Representations for Affinity Prediction and Inhibitor Generation: 3D Effects in a 1D Model. J Chem Inf Model 2021. [PMID: 34905358 DOI: 10.1021/acs.jcim.1c00889] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]

Number	Citing Articles
1	Zeng X, Wang F, Luo Y, Kang SG, Tang J, Lightstone FC, Fang EF, Cornell W, Nussinov R, Cheng F. Deep generative molecular design reshapes drug discovery. Cell Rep Med 2022;3:100794. [PMID: 36306797 DOI: 10.1016/j.xcrm.2022.100794] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
2	Born J, Shoshan Y, Huynh T, Cornell WD, Martin EJ, Manica M. On the Choice of Active Site Sequences for Kinase-Ligand Affinity Prediction. J Chem Inf Model 2022. [PMID: 36098536 DOI: 10.1021/acs.jcim.2c00840] [Reference Citation Analysis]
3	Dong L, Qu X, Wang B. XLPFE: A Simple and Effective Machine Learning Scoring Function for Protein–Ligand Scoring and Ranking. ACS Omega. [DOI: 10.1021/acsomega.2c01723] [Reference Citation Analysis]
4	Torrisi M, de la Vega de León A, Climent G, Loos R, Panjkovich A. Improving the Assessment of Deep Learning Models in the Context of Drug-Target Interaction Prediction.. [DOI: 10.1101/2022.04.20.488898] [Reference Citation Analysis]