DrugEx v2: de novo design of drug molecules by pareto-based multi-objective reinforcement learning in polypharmacology von Liu, X., Ye, K., Vlijmen, H.W.T. van, Emmerich, M.T.M., IJzerman, A.P., Westen, G.J.P. van

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Quantitative prediction of selectivity between the A1 and A2A adenosine receptors von Burggraaff, L., Vlijmen, H.W.T. van, IJzerman, A.P., Westen, G.J.P. van

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Titration calculations of foot-and-mouth disease virus capsids and their stabilities as a function of pH von Van Vlijmen, H.W.T, Curry, S, Schaefer, M, Karplus, M

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An exploration strategy improves the diversity of de novo ligands using deep reinforcement learning: a case for the adenosine A2A receptor von Liu, X., Ye, K., Vlijmen, H.W.T. van, IJzerman, A.P., Westen, G.J.P. van

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Identification of novel small molecule inhibitors for solute carrier SGLT1 using proteochemometric modeling von Burggraaff, L., Oranje, P., Gouka, R., Pijl, P. van der, Geldof, M., Vlijmen, H.W.T. van, IJzerman, A.P., Westen, G.J.P. van

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Fysica en Filatelie von Vlijmen, H.W.T. van

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Predicting binding affinities for GPCR ligands using free-energy perturbation von Lenselink, E.B., Louvel, J.A., Forti, A.F., Veldhoven, J.P.D. van, Vries, H. de, Mulder-Krieger, T., McRobb, F.M., Negri, A., Goose, J., Abel, R., Vlijmen, H.W.T. van, Wang, L., Harder, E., Sherman, W., IJzerman, A.P., Beuming, T.

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Significantly Improved HIV Inhibitor Efficacy Prediction Employing Proteochemometric Models Generated From Antivirogram von Westen, G.J.P. van, Hendriks, A., Wegner, J.K., IJzerman, A.P., Vlijmen, H.W.T. van, Bender, A.

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Using multiobjective optimization and energy minimization to design an isoform-selective ligand of the 14-3-3 Protein von Sanchez-Faddeev, H., Emmerich, Michael T.M., Verbeek, F.J., Henry, A.H., Grimshaw, S., Spaink, H.P., Vlijmen, H.W.T. van, Bender, A.

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Annotation of Allosteric Compounds to Enhance Bioactivity Modeling for Class A GPCRs von Burggraaff, L., Veen, A. van, Lam, C.C., Vlijmen, H.W.T., IJzerman, A.P., Westen, G.J.P. van

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