update

model_cards/article.md

@@ -27,7 +27,7 @@
 
 **Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**: Trained by the original authors with the default parameters provided [on GitHub](https://github.com/microsoft/molecule-generation).
 
-**Paper or other resource for more information**: Learning to Extend Molecular Scaffolds with Structural Motifs (ICLR 2022).
+**Paper or other resource for more information**: [Learning to Extend Molecular Scaffolds with Structural Motifs (ICLR 2022)](https://openreview.net/forum?id=ZTsoE8G3GG).
 
 **License**: MIT
 

model_cards/description.md

@@ -1,6 +1,6 @@
 <img align="right" src="https://raw.githubusercontent.com/GT4SD/gt4sd-core/main/docs/_static/gt4sd_logo.png" alt="logo" width="120" >
 
-MoLeR (Maziarz et al., (2022), *ICLR*) is a graph-based molecular generative model that can be conditioned (primed) on scaffolds. This model is provided and distributed by the **GT4SD** (Generative Toolkit for Scientific Discovery).
+MoLeR ([Maziarz et al., (2022), *ICLR*](https://openreview.net/forum?id=ZTsoE8G3GG)) is a graph-based molecular generative model that can be conditioned (primed) on scaffolds. This model is provided and distributed by the **GT4SD** (Generative Toolkit for Scientific Discovery).
 
 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.