For single-property manipulation, we can simply take the identified direction, but when multiple properties correlate with each other, we need to determine whether the two directions are entangled or disentangled. We can then simply take the disentangled and positively correlated attributions of the directions as the new direction: Baselines. We include two baseline methods of identifying latent direction that governs the molecular property and one gradient-based method, which optimizes the molecular property of the generated molecules via gradient ascent/descent for comparisons. Random manipulation randomly samples latent directions for molecular properties. Largest range manipulation draws latent vectors from the training set and defines the directions via calculating the direction between one molecule with the largest property score and another molecule with the smallest property score for each molecular property. Gradient-based method optimizes the molecular property of the generated molecules by searching a latent vector with the optimized molecular property via gradient ascent/descent.