  Structure-based molecular ML (SBML) models can be highly sensitive to input
geometries and give predictions with large variance. We present an approach to
mitigate the challenge of selecting conformations for such models by generating
conformers that explicitly minimize predictive uncertainty. To achieve this, we
compute estimates of aleatoric and epistemic uncertainties that are
differentiable w.r.t. latent posteriors. We then iteratively sample new latents
in the direction of lower uncertainty by gradient descent. As we train our
predictive models jointly with a conformer decoder, the new latent embeddings
can be mapped to their corresponding inputs, which we call \textit{MoleCLUEs},
or (molecular) counterfactual latent uncertainty explanations
\citep{antoran2020getting}. We assess our algorithm for the task of predicting
drug properties from 3D structure with maximum confidence. We additionally
analyze the structure trajectories obtained from conformer optimizations, which
provide insight into the sources of uncertainty in SBML.
