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Molecular Optimization on PMO

0.98Albuterol Similarityf-RAG
0.83440.87220.910.9478Jun 29, 2026

Evaluation results

MethodLinks
f-RAG2026.060.980.750.780.940.990.860.970.960.870.70.340.320.670.870.680.940.820.580.60.580.450.630.4916.93
GenMol2026.060.940.810.830.9610.90.990.940.830.910.40.40.980.880.720.940.820.630.590.70.870.820.5818.36
Genetic GFNTraining Dataset=SMILES & ZINC, Encoding=SMILES2026.060.940.670.770.680.970.830.880.970.890.710.350.270.760.850.590.950.780.560.640.580.510.120.5415.81
Genetic GFNTraining Dataset=Random Dataset, Encoding=SMILES2026.060.920.680.580.630.970.80.790.980.890.570.340.270.750.840.570.940.770.60.420.650.5300.4914.97
molGA2026.060.90.690.570.650.940.830.840.880.870.780.260.30.60.840.550.940.80.530.580.520.4300.5214.71
Genetic GFNTraining Dataset=Random Dataset, Encoding=GGS, Model Architecture=Transformer, Regularization=DCD, DEX, -KL, Input Features=Descriptors2026.060.90.560.680.650.960.790.80.980.830.620.280.320.840.850.560.940.790.560.50.40.4100.4914.7
Genetic GFNTraining Dataset=Random Dataset, Encoding=GGS2026.060.890.60.590.780.960.770.860.980.810.660.320.330.850.870.580.940.770.580.480.450.420.10.5315.11
REINVENT2026.060.880.640.710.670.950.780.870.850.640.780.360.280.620.840.540.940.760.560.020.530.440.180.3614.2
Genetic GFNTraining Dataset=Random Dataset, Encoding=GGS, Model Architecture=Transformer, Regularization=DCD, DEX, -KL2026.060.870.580.710.70.960.790.830.970.780.660.280.320.860.860.550.940.790.590.450.440.390.050.514.9
Genetic GFNTraining Dataset=Random Dataset, Encoding=GGS, Model Architecture=Transformer2026.060.840.580.60.650.960.780.830.980.820.650.30.330.850.860.560.940.790.590.460.440.390.030.5114.75