Bayesian optimisation is an important decision-making tool for high-stakes applications in drug discovery and materials design. An oft-overlooked modelling consideration however is the representation of input-dependent or heteroscedastic aleatoric uncertainty. The cost of misrepresenting this uncertainty as being homoscedastic could be high in drug discovery applications where neglecting heteroscedasticity in high throughput virtual screening could lead to a failed drug discovery program. In this paper, we propose a heteroscedastic Bayesian optimisation scheme which both represents and penalises aleatoric noise in the suggestions.Our scheme features a heteroscedastic Gaussian Process (GP) as the surrogate model in conjunction with two acquisition heuristics. First, we extend the augmented expected improvement (AEI) heuristic to the heteroscedastic setting and second, we introduce a new acquisition function, aleatoric-penalised expected improvement (ANPEI) based on a simple scalarisation of the performance and noise objective. Both methods penalise aleatoric noise in the suggestions and yield improved performance relative to a naive implementation of homoscedastic Bayesian optimisation on toy problems as well as a real-world optimisation problem.
翻译:Bayesian 优化是药物发现和材料设计中高吸量应用的一个重要决策工具。 但是,人们所关注的建模考量却代表了依赖投入的或超摄性高吸胶的不确定性。在药物发现应用中,将这种不确定性误报为同系物的成本可能很高,在药物发现应用中,忽视高吸量虚拟筛选中的超摄性可能会导致药物发现方案失败。在本文中,我们建议采用一种超脱性巴耶西亚优化方案,它既代表也惩罚建议中的消散性噪音。我们的计划将一种超脱性高压过程(GP)作为替代模型,同时结合两种收购超感性能模型。首先,我们扩大预期的改进(AEI)与高吸量虚拟筛选环境的超常性能可能会导致药物发现方案失败。 其次,我们引入一种新的获取功能,即耐受抑制性地预期的改进(ANPEI)方案,其基础是简单调整性能和噪声目标。两种方法都把超采性压过程作为替代模型的模型,同时将Alimicalizalimalizalizalizalizalizalization imation Appalization Applistemation 。