Neural processes (NPs) are models for transfer learning with properties reminiscent of Gaussian Processes (GPs). They are adept at modelling data consisting of few observations of many related functions on the same input space and are trained by minimizing a variational objective, which is computationally much less expensive than the Bayesian updating required by GPs. So far, most studies of NPs have focused on low-dimensional datasets which are not representative of realistic transfer learning tasks. Drug discovery is one application area that is characterized by datasets consisting of many chemical properties or functions which are sparsely observed, yet depend on shared features or representations of the molecular inputs. This paper applies the conditional neural process (CNP) to DOCKSTRING, a dataset of docking scores for benchmarking ML models. CNPs show competitive performance in few-shot learning tasks relative to supervised learning baselines common in chemoinformatics, as well as an alternative model for transfer learning based on pre-training and refining neural network regressors. We present a Bayesian optimization experiment which showcases the probabilistic nature of CNPs and discuss shortcomings of the model in uncertainty quantification.
Inverse design of 3d molecular structures with conditional generative neural networks
Technische Universität Berlin（柏林工业大学）；Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；Korea University（高丽大学）；Max-Planck-Institut für Informatik（马克斯·普朗克信息学研究所）
Goal-directed Generation of Discrete Structures with Conditional Generative Models
University of Geneva（日内瓦大学）；University of Applied Sciences Western Switzerland（瑞士西部应用科学大学）；University College London（伦敦大学学院）；Alan Turing Institute（阿兰·图灵研究所）
Generating equilibrium molecules with deep neural networks
Technische Universität Berlin（柏林工业大学）
发现具有理想性质的原子体系是化学和材料科学中的一个重大挑战。在这里，我们介绍了一种新的自回归卷积深度神经网络架构，它通过在三维空间中顺序放置原子来生成分子平衡结构。该模型用易于处理的条件概率估计了分子构型上的联合概率，条件概率仅取决于原子与其核电荷之间的距离。它将最先进的原子神经网络的概念与图像和语音的自回归生成模型相结合。我们证明，该架构是能够产生接近平衡的C $_7$ O $_2$ H $_{10}$的结构异构体的分子。
