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  <img align="right" src="https://raw.githubusercontent.com/GT4SD/gt4sd-core/main/docs/_static/gt4sd_logo.png" alt="logo" width="120" >
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- MoLeR (Maziarz et al., (2022), *ICLR*) is a graph-based molecular generative model that can be conditioned (primed) on scaffolds. This model r is provided and distributed by the **GT4SD** (Generative Toolkit for Scientific Discovery).
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  For **examples** and **documentation** of the model parameters, please see below.
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  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.
 
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  <img align="right" src="https://raw.githubusercontent.com/GT4SD/gt4sd-core/main/docs/_static/gt4sd_logo.png" alt="logo" width="120" >
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+ 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).
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  For **examples** and **documentation** of the model parameters, please see below.
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  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.