[PaccMann^RL](https://github.com/PaccMann/paccmann_rl) is a language-based molecular generative model that can be conditioned (primed) on protein targets or gene expression profiles and produces molecules with high affinity toward the context vector. This model has been developed at IBM Research and is distributed by the **GT4SD** (Generative Toolkit for Scientific Discovery) team. For details please see the two publications: - [Born et al., (2021), *iScience*](https://www.cell.com/iscience/fulltext/S2589-0042(21)00237-6) for the model conditionable on gene expression profiles. - [Born et al., (2021), *Machine Learning: Science & Technology*](https://iopscience.iop.org/article/10.1088/2632-2153/abe808/meta) for the model conditionable on protein targets. For **examples** and **documentation** of the model parameters, please see below. Moreover, we provide a **model card** ([Mitchell et al. (2019)](https://dl.acm.org/doi/abs/10.1145/3287560.3287596?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs)) at the bottom of this page.