vNN-ADMET

Build Regression Model Results

Job Name: MRTD (Current Version) Show model compounds

Model compounds

Name	SMILES	Property
mrtd0001	COc1ccc2c(c1)c(CC(=O)OCC(=O)O)c(C)n2C(=O)c3ccc(Cl)...	-5.141787994
mrtd0002	NCC(CC(=O)O)c1ccc(Cl)cc1	-5.205873169
mrtd0003	CCNC(=O)N(CCCN(C)C)C(=O)C1CC2C(Cc3c[nH]c4cccc2c34)...	-7.353596274
mrtd0004	CN(C)NN=C1N=CN=C1C(=O)N	-4.607295812
mrtd0005	CCOP(=O)(OCC)SCC[N+](C)(C)C	-6.186070395
mrtd0006	CC(=O)OCC(CCn1cnc2cnc(N)nc12)COC(=O)C	-4.109013631
mrtd0007	FC(F)(F)CN1C(=O)CN=C(c2ccccc2)c3cc(Cl)ccc13	-5.120941208
mrtd0008	CC(CCc1ccccc1)NCC(O)c2ccc(O)c(c2)C(=O)N	-3.914350378
mrtd0009	CCCCCCC(C)(C)c1cc(O)c2C3CC(=O)CCC3C(C)(C)Oc2c1	-6.571173878
mrtd0010	CN1CCN(CC1)C2=C3C=C(C)SC3=Nc4ccccc4N2	-6.096816195

Regression

0.1	0.1	0.846	0.018
0.1	0.2	0.846	0.018
0.1	0.3	0.846	0.018
0.1	0.4	0.846	0.018
0.1	0.5	0.846	0.018
0.1	0.6	0.846	0.018
0.1	0.7	0.846	0.018
0.1	0.8	0.846	0.018
0.1	0.9	0.846	0.018
0.1	1.0	0.846	0.018
0.2	0.1	0.869	0.068
0.2	0.2	0.896	0.068
0.2	0.3	0.901	0.068
0.2	0.4	0.902	0.068
0.2	0.5	0.903	0.068
0.2	0.6	0.904	0.068
0.2	0.7	0.904	0.068
0.2	0.8	0.904	0.068
0.2	0.9	0.904	0.068
0.2	1.0	0.904	0.068
0.3	0.1	0.868	0.206
0.3	0.2	0.879	0.206
0.3	0.3	0.879	0.206
0.3	0.4	0.879	0.206
0.3	0.5	0.879	0.206
0.3	0.6	0.879	0.206
0.3	0.7	0.879	0.206
0.3	0.8	0.878	0.206
0.3	0.9	0.878	0.206
0.3	1.0	0.878	0.206
0.4	0.1	0.84	0.388
0.4	0.2	0.854	0.388
0.4	0.3	0.855	0.388
0.4	0.4	0.855	0.388
0.4	0.5	0.854	0.388
0.4	0.6	0.854	0.388
0.4	0.7	0.853	0.388
0.4	0.8	0.853	0.388
0.4	0.9	0.853	0.388
0.4	1.0	0.853	0.388
0.5	0.1	0.824	0.529
0.5	0.2	0.843	0.529
0.5	0.3	0.846	0.529
0.5	0.4	0.841	0.529
0.5	0.5	0.837	0.529
0.5	0.6	0.834	0.529
0.5	0.7	0.832	0.529
0.5	0.8	0.83	0.529
0.5	0.9	0.829	0.529
0.5	1.0	0.828	0.529
0.6	0.1	0.782	0.67
0.6	0.2	0.798	0.67
0.6	0.3	0.795	0.67
0.6	0.4	0.783	0.67
0.6	0.5	0.774	0.67
0.6	0.6	0.768	0.67
0.6	0.7	0.764	0.67
0.6	0.8	0.762	0.67
0.6	0.9	0.76	0.67
0.6	1.0	0.759	0.67
0.7	0.1	0.752	0.822
0.7	0.2	0.767	0.822
0.7	0.3	0.767	0.822
0.7	0.4	0.757	0.822
0.7	0.5	0.748	0.822
0.7	0.6	0.741	0.822
0.7	0.7	0.737	0.822
0.7	0.8	0.734	0.822
0.7	0.9	0.731	0.822
0.7	1.0	0.73	0.822
0.8	0.1	0.711	0.977
0.8	0.2	0.741	0.977
0.8	0.3	0.749	0.977
0.8	0.4	0.728	0.977
0.8	0.5	0.7	0.977
0.8	0.6	0.676	0.977
0.8	0.7	0.659	0.977
0.8	0.8	0.645	0.977
0.8	0.9	0.636	0.977
0.8	1.0	0.628	0.977
0.9	0.1	0.704	1.0
0.9	0.2	0.743	1.0
0.9	0.3	0.751	1.0
0.9	0.4	0.681	1.0
0.9	0.5	0.597	1.0
0.9	0.6	0.54	1.0
0.9	0.7	0.509	1.0
0.9	0.8	0.49	1.0
0.9	0.9	0.478	1.0
0.9	1.0	0.471	1.0
1.0	0.1	0.704	1.0
1.0	0.2	0.743	1.0
1.0	0.3	0.739	1.0
1.0	0.4	0.675	1.0
1.0	0.5	0.635	1.0
1.0	0.6	0.595	1.0
1.0	0.7	0.56	1.0
1.0	0.8	0.525	1.0
1.0	0.9	0.489	1.0
1.0	1.0	0.449	1.0