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+framework+maps+molecular+graphs+to+Euclidean+point+clouds+via+synthetic+conformer+coordinates+and+learns+the+inverse+map+using+an+E(n)-Equivariant+Graph+Neural+Network+(EGNN).+The+induced+point+cloud-structured+latent+space+is+well-suited+to+apply+existing+3D+molecular+generative+models.+This+approach+simplifies+the+graph+generation+problem+-+without+relying+on+molecular+fragments+nor+autoregressive+decoding+-+into+a+point+cloud+generation+problem+followed+by+node+and+edge+classification+tasks.+Further%252C+we+propose+a+novel+similarity-constrained+optimization+scheme+for+3D+diffusion+models+based+on+inpainting+and+guidance.+As+a+concrete+implementation+of+our+framework%252C+we+develop+EDM-SyCo+based+on+the+E(3)+Equivariant+Diffusion+Model+(EDM).+EDM-SyCo+achieves+state-of-the-art+performance+in+distribution+learning+of+molecular+graphs%252C+outperforming+the+best+non-autoregressive+methods+by+more+than+30%2525+on+ZINC250K+and+16%2525+on+the+large-scale+GuacaMol+dataset+while+improving+conditional+generation+by+up+to+3.9+times.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.a0c733432f2539062319bfa9b2e6c663%3frik%3dxHMPXeeKmOndHA%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Molecular+Graph+Model&FORM=IRPRST&ck=3A2FEC258F5D6F63D778DFFFF861E1EB&selectedIndex=27&itb=0)
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