Merge pull request #45 from Jonas-Verhellen/patch-1

Update paperlist.yml

_data/paperlist.yml

@@ -1,5 +1,41 @@
 papers:
 
+- abstract: 'In recent years, there have been considerable academic and industrial 
+
+research efforts to develop novel generative models for high-performing, small molecules. 
+
+Traditional, rules-based algorithms such as genetic algorithms [Jensen, Chem. Sci., 2019, 
+
+12, 3567-3572] have, however, been shown to rival deep learning approaches in terms of both 
+
+efficiency and potency. In previous work, we showed that the addition of a quality-diversity 
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+archive to a genetic algorithm resolves stagnation issues and substantially increases search 
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+efficiency [Verhellen, Chem. Sci., 2020, 42, 11485-11491]. In this work, we expand on these insights 
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+and leverage the availability of bespoke kernels for small molecules [Griffiths, Adv. Neural. Inf. 
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+Process. Syst., 2024, 36] to integrate Bayesian optimisation into the quality-diversity process. 
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+This novel generative model, which we call Bayesian Illumination, produces a larger diversity of 
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+high-performing molecules than standard quality-diversity optimisation methods. In addition, 
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+we show that Bayesian Illumination further improves search efficiency com- pared to previous 
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+generative models for small molecules, including deep learning approaches, genetic algorithms, 
+
+and standard quality-diversity methods.'
+  authors:
+  - Jonas Verhellen
+  bibtex: "@article{Samvelyan2024Rainbow,\n\ttitle={Bayesian Illumination: Inference and 
+    \ Quality-Diversity Accelerate Generative Molecular Models},\n\tauthor={Verhellen, 
+    \ Jonas},\n\tyear={2024} }"
+  pdfurl: https://chemrxiv.org/engage/chemrxiv/article-details/667c2bdd5101a2ffa88fae63
+  title: "Bayesian Illumination: Inference and Quality-Diversity Accelerate Generative Molecular Models"
+  year: 2024
+
 - abstract: 'As large language models (LLMs) become increasingly prevalent across
     many