Oliver Watson | Searcharxiv

arXiv2026

Leveraging deep generative modelling at the agent level in discrete-event simulations

Emulators are often invoked to facilitate parameter inference in computationally intensive agent-based simulations (ABSs). Most approaches, however, require a large sample of pre-existing outputs on which such emulators can be trained. For computationally intensive simulations, this sample can be prohibitively expensive to generate. We propose a new approach to leverage emulators at the agent level in event-based ABSs to reduce the computational requirements of the simulation itself. We demonstrate this in a computationally intensive ``multi-disease'' ABS, Thanzi La Onse, which self-consistently simulates a wide range of medical conditions in a population. By applying conditional tabular generative adversarial networks to replace the modelling of one of the simulated causes of ill health, road traffic injuries (RTIs), we are able to improve its simulation run-time by 86.6% (95% bootstrap confidence interval [85.2, 88.1]), while recovering the evolution of cause-specific mean disability adjusted life years with a 13% error, despite the low assumed incidence of fatal RTIs (~2%). Crucially, we do so while retaining the full ABS framework, and correctly recovering multi-disease dynamics. This represents a crucial step towards enabling full-model emulators for very computationally intensive simulations, and making large ABS amenable to rigorous parameter inference and a full understanding of parameter sensitivity.

q-bio.QM↗

arXiv2018

A decision theoretic approach to model evaluation in computational drug discovery

Artificial intelligence, trained via machine learning or computational statistics algorithms, holds much promise for the improvement of small molecule drug discovery. However, structure-activity data are high dimensional with low signal-to-noise ratios and proper validation of predictive methods is difficult. It is poorly understood which, if any, of the currently available machine learning algorithms will best predict new candidate drugs. 25 publicly available molecular datasets were extracted from ChEMBL. Neural nets, random forests, support vector machines (regression) and ridge regression were then fitted to the structure-activity data. A new validation method, based on quantile splits on the activity distribution function, is proposed for the construction of training and testing sets. Model validation based on random partitioning of available data favours models which overfit and `memorize' the training set, namely random forests and deep neural nets. Partitioning based on quantiles of the activity distribution correctly penalizes models which can extrapolate onto structurally different molecules outside of the training data. This approach favours more constrained models, namely ridge regression and support vector regression. In addition, our new rank-based loss functions give considerably different results from mean squared error highlighting the necessity to define model optimality with respect to the decision task at hand. Model performance should be evaluated from a decision theoretic perspective with subjective loss functions. Data-splitting based on the separation of high and low activity data provides a robust methodology for determining the best extrapolating model. Simpler, traditional statistical methods such as ridge regression outperform state-of-the-art machine learning methods in this setting.

stat.AP↗