  This paper proposes a machine learning (ML) method to predict stable
molecular geometries from their chemical composition. The method is useful for
generating molecular conformations which may serve as initial geometries for
saving time during expensive structure optimizations by quantum mechanical
calculations of large molecules. Conformations are found by predicting the
local arrangement around each atom in the molecule after trained from a
database of previously optimized small molecules. It works by dividing each
molecule in the database into minimal building blocks of different type. The
algorithm is then trained to predict bond lengths and angles for each type of
building block using an electro-topological fingerprint as descriptor. A
conformation is then generated by joining the predicted blocks. Our model is
able to give promising results for optimized molecular geometries from the
basic knowledge of the chemical formula and connectivity. The method trends to
reproduce interatomic distances within test blocks with RMSD under $0.05$ \r{A}
