Creating EDM has a huge sound design barrier that gets in the way sometimes.
A great way around this can be to utilize splice. However, if you find a synth bass sound you like, transposing can squash some of the beauty of the sample.
You can also try to recreate the sound using a VST and some effects.
Wouldn't it be cool if we just had a very simple optimizer, where each trainable parameter was a knob on the VST / effects? Even a few hundred trainable parameters (TINY for a machine learning model) would provide the ability to closely replicate most synth sounds found on splice for example.
Training would be simple: you simply 'overfit' your model ('synth preset') on a single sample.
Loss function: feed the model the same fundamental frequency (MIDI note) & compare waveforms.
For starters, this could be just an evolutionary algorithm. Nothing even needs to be differentiable.
Current work worth looking into: