Searching new molecules in areas like drug discovery often starts from the core structures of candidate molecules to optimize the properties of interest. The way as such has called for a strategy of designing molecules retaining a particular scaffold as a substructure. On this account, our present work proposes a scaffold-based molecular generative model. The model generates molecular graphs by extending the graph of a scaffold through sequential additions of vertices and edges. In contrast to previous related models, our model guarantees the generated molecules to retain the given scaffold with certainty. Our evaluation of the model using unseen scaffolds showed the validity, uniqueness, and novelty of generated molecules as high as the case using seen scaffolds. This confirms that the model can generalize the learned chemical rules of adding atoms and bonds rather than simply memorizing the mapping from scaffolds to molecules during learning. Furthermore, despite the restraint of fixing core structures, our model could simultaneously control multiple molecular properties when generating new molecules.
Scaffold-Conditioned Preference Triplets for Controllable Molecular Optimization with Large Language Models
National Key Laboratory of Parallel and Distributed Processing（平行与分布式处理国家重点实验室）；College of Computer Science and Technology（计算机科学与技术学院）；National University of DefenseTechnology（国防科技大学）；DP Technology（DP科技）
Scaffold-Aware Generative Augmentation and Reranking for Enhanced Virtual Screening
Yale University（耶鲁大学）；University of Oregon（俄勒冈大学）；Broad Institute of MIT and Harvard（MIT和哈佛大学Broad研究所）；Leipzig University（莱比锡大学）；Vanderbilt University（范德比大学）
MolHop: Scaffold-Hopping Molecular Design with Agentic Reinforcement Learning
State Key Laboratory of Physical Chemistry of Solid Surface, College of Chemistry and Chemical Engineering, Xiamen University（厦门大学化学化工学院固体表面物理化学国家重点实验室）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究院）
Differentiable Scaffolding Tree for Molecular Optimization
Amplitude（Amplitude公司）；Massachusetts Institute of Technology（麻省理工学院）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Georgia Institute of Technology（佐治亚理工学院）
Learning to Select Source Domains: Proxy-Rewarded Policy Optimization for Molecular OOD Generalization
Department of Data Science and Artificial Intelligence, Monash University（墨尔本大学数据科学与人工智能系）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Life Sciences and Technology, Tongji University（同济大学生命科学与技术学院）
PolymerGPT: Multi-property Optimization with a Decoder-Based GPT Model for Generative Polymer Design
Texas A&M University（得克萨斯农工大学）；University of Pittsburgh（匹兹堡大学）；Virginia Tech（弗吉尼亚理工大学）；University of North Carolina Greensboro（北卡罗来纳大学格林斯伯勒分校）；Old Dominion University（奥多明尼昂大学）
Generating readily synthesizable small molecule fluorophore scaffolds with reinforcement learning
Department of Computer Science, Stanford University（计算机科学系，斯坦福大学）；Department of Molecular and Cellular Physiology, Stanford University（分子与细胞生理学系，斯坦福大学）；Department of Physics, Stanford University（物理系，斯坦福大学）；Department of Biomedical Data Science, Stanford University（生物医学数据科学系，斯坦福大学）；Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus（生物化学与分子遗传学系，科罗拉多大学安舒茨医学校区）；Department of Pharmacology, University of Colorado Anschutz Medical Campus（药理学系，科罗拉多大学安舒茨医学校区）；Department of Pharmaceutical Sciences, Skaggs School of Pharmacy, University of Colorado Anschutz Medical Campus（药学系，斯克格斯药学院，科罗拉多大学安舒茨医学校区）；BioFrontiers Institute, University of Colorado Boulder（生物前沿研究院，科罗拉多大学波德分校）；Greenstone Biosciences（Greenstone 生物科技公司）；Stanford University（斯坦福大学）
Interpolating GANs to Scaffold Autotelic Creativity
MIT Media Lab（麻省理工学院媒体实验室）
生成对抗网络（GANs）建模的潜在空间代表了一个很大的可能性空间。通过内插由GANs生成的类别，可以创建新颖的混合图像。我们提出了“满足的动物”，一个休闲的创作者建立在BigGAN的插值，可以生成新颖的，杂交动物称为动物有效地搜索这个可能性空间。与传统的休闲创作者一样，该系统支持简单的创意流程，鼓励快速探索可能性空间。用户可以发现新的动物，创建自己的动物，并分享他们对动物的美学，情感和形态特征的反应。当用户向系统提供输入时，系统适应并改变生成动物的类别的分布。作为第一批基于GAN的休闲创作者之一，Meet the Glanimals是休闲创作者如何利用人类策展和公民科学在大的可能性空间内发现新奇文物的一个例子。
Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design
School of Computer Science and Technology, East China Normal University（东华师范大学计算机科学与技术学院）；Shanghai Institute of AI for Education, East China Normal University（东华师范大学上海人工智能教育研究所）；AI Lab, Research Center for Industries of the Future, Westlake University（西湖大学未来产业研究中心人工智能实验室）
Multi-Objective Latent Space Optimization of Generative Molecular Design Models
Texas A&M University（德克萨斯农工大学）；Brookhaven National Laboratory（布鲁克海文国家实验室）；Frederick National Laboratory（弗雷德里克国家实验室）；Argonne National Laboratory（阿贡国家实验室）
Generative Enriched Sequential Learning (ESL) Approach for Molecular Design via Augmented Domain Knowledge
National Research Council Canada（加拿大国家研究委员会）；University of Ottawa（渥太华大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；Brock University（布鲁克大学）
