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Datasets:
scikit-fingerprints
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ExpansionRx_OpenADMET_MLM_CLint

Tasks:
Tabular Regression
Graph Machine Learning
Text Classification
Modalities:
Text
Formats:
csv
Size:
1K - 10K
Tags:
chemistry
biology
medical
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Auto-converted to Parquet Duplicate
Molecule name
stringlengths
9
9
SMILES
stringlengths
22
88
MLM CLint
float64
0
10.4k
E-0001321
CN1CCC[C@H]1COc1ccc(-c2nc3cc(-c4ccc5[nH]c(-c6ccc(O)cc6)nc5c4)ccc3[nH]2)cc1
182.3
E-0001780
COc1ccc2c(c1)c1cc3cnccc3c(C)c1n2C
1,351.1
E-0001827
Cc1c2ccncc2cc2c3cc(OCCCN4CCN(C)CC4)ccc3n(C)c12
193.5
E-0002036
CN(C)CCCOc1ccc2nc(-c3ccc(-c4nc5ccc(OCCCN(C)C)cc5[nH]4)c(F)c3)[nH]c2c1
162
E-0002269
CN(C)CC(O)COc1ccc2nc(-c3ccc(-c4nc5ccc(OCC(O)CN(C)C)cc5[nH]4)c(F)c3)[nH]c2c1
64.2
E-0002284
COc1ccc(-c2nc3ccc(OCCCN(C)C)cc3[nH]2)cc1NC(=O)c1cccc(NC(=O)c2ncc[nH]2)c1
66.1
E-0002290
Cc1ccc(-c2nc3ccccc3[nH]2)cc1NC(=O)c1cccc(-c2nc3cc(OCCCN(C)C)ccc3[nH]2)c1
167
E-0002377
COc1ccc2nc(-c3ccc(-c4nc5ccc(OCCCC(=N)N)cc5[nH]4)cc3)[nH]c2c1
77.7
E-0003079
CN(C)CCCOc1ccc2nc(-c3cccc4nc(-c5ccncc5)[nH]c34)[nH]c2c1
342
E-0003089
CN(C)CCCOc1ccc(-c2nc3cc(-c4ccc5[nH]c(N)nc5c4)ccc3[nH]2)cc1
82.8
E-0003090
CN(C)CCCOc1ccc(-c2nc3cc(-c4ccc5[nH]cnc5c4)ccc3[nH]2)cc1
336
E-0010021
CN(C)Cc1ccc(CNC(=O)Nc2ccc3nnsc3c2)cc1
180
E-0010022
CN(C)CCOc1ccc(CNC(=O)Nc2ccc3nnsc3c2)cc1
250.1
E-0010024
CC(NC(=O)Nc1ccc2nnsc2c1)c1cnn(C)c1
36
E-0010025
O=C(NCc1cn[nH]c1)Nc1ccc2nnsc2c1
2,044.5
E-0010059
CN1CCC(C(=O)NCc2cccc(CNC(=O)Nc3ccc4nnsc4c3)c2)CC1
54.8
E-0010060
O=C(NCc1cccc(CNC(=O)c2nc3ccccc3[nH]2)c1)Nc1ccc2nnsc2c1
658.3
E-0010062
CN(C)CCCOc1ccc(CNC(=O)Nc2ccc3ncccc3c2)cc1
39.4
E-0010066
CN(C)CCCOc1ccc(CNC(=O)Nc2ccc3ncsc3c2)cc1
69.1
E-0010071
COc1ccc2nc(-c3ccc(-c4nc5ccc(OCCCNC(=N)N(C)C)cc5[nH]4)cc3)[nH]c2c1
96.7
E-0010120
CN(C)CCCOc1ccc(CNC(=O)Nc2ccc3cnccc3c2)cc1
675.8
E-0010121
NCc1cccc(CNC(=O)Nc2ccc3ncccc3c2)c1
439
E-0010138
COc1ccc2c(c1)c1cc3c(C(=O)N[C@H](C)CN(C)C)nccc3c(C)c1n2C
4,085.9
E-0010347
Cc1c2ccncc2cc2c3cc(OCCN(C)C)ccc3n(C)c12
418.1
E-0010358
Cc1c2ccncc2cc2c3cc(OCCCN4CCOCC4)ccc3n(C)c12
517
E-0010360
Cc1c2ccncc2cc2c3cc(OCCCN4CCCC4)ccc3n(C)c12
273.8
E-0010361
CCN(CC)CCCOc1ccc2c(c1)c1cc3cnccc3c(C)c1n2C
243.6
E-0010362
Cc1c2ccncc2cc2c3cc(OCCN4CCOCC4)ccc3n(C)c12
685
E-0010455
Cc1c2ccncc2cc2c3cc(OCC(N)=O)ccc3n(C)c12
399
E-0010457
Cc1c2ccncc2cc2c3cc(C(=O)NCCN(C)C)ccc3n(C)c12
325
E-0010464
COc1ccc2c(c1)c1cc(C(=O)NCCCN(C)C)nc(C)c1n2C
481.7
E-0010481
Cc1nc(C(=O)NCCCN(C)C)cc2c3cc(OCCCN(C)C)ccc3n(C)c12
275.1
E-0010482
Cc1nc(C(=O)NCCN(C)C)cc2c3cc(OCCCN(C)C)ccc3n(C)c12
216.7
E-0010495
Cc1c2ccncc2cc2c3cc(OCC4=NCCN4)ccc3n(C)c12
233
E-0010519
CN(C)CCCNC(=O)c1cc2c3cc(OCCCN(C)C)ccc3n(C)c2cn1
128.9
E-0010520
CN(C)CCCOc1ccc2c(c1)c1cc(C(=O)NCCN(C)C)ncc1n2C
41.2
E-0010559
CNC(=O)COc1ccc2c(c1)c1cc3cnccc3c(C)c1n2C
737.5
E-0010561
COc1ccc2c(c1)c1cc3c(C(=O)NCCN(C)C)nccc3c(C)c1n2C
3,325.1
E-0010562
Cc1c2ccncc2cc2c3cc(OCCN4CCCCC4)ccc3n(C)c12
297.7
E-0010567
Cc1c2ccncc2cc2c3cc(OCCN)ccc3n(C)c12
144.2
E-0010568
COc1ccc2c(c1)c1cc3c(C(=O)NCCCN(C)C)nccc3c(C)c1n2C
1,426.9
E-0010569
Cc1c2ccncc2cc2c3cc(OCCN4CCN(C)CC4)ccc3n(C)c12
266.8
E-0010578
CCN(CC)CCOc1ccc2c(c1)c1cc3cnccc3c(C)c1n2C
237.5
E-0010584
COc1ccc2c(c1)c1cc3ccncc3c(C)c1n2C
1,456.8
E-0010596
Cc1c2ccncc2cc2c3ccc(OCC4=NCCN4)cc3n(C)c12
611.9
E-0010600
COc1ccc2oc3c(C)c4ccncc4cc3c2c1
273.4
E-0010604
Cc1c2ccncc2cc2c3cc(OC[C@H]4CCCN4)ccc3n(C)c12
211.2
E-0010607
Cc1c2ccncc2cc2c3c(OCC4=NCCN4)cccc3n(C)c12
578.9
E-0010611
Cc1c2ccncc2cc2c3cc(OC[C@@H]4CCCN4)ccc3n(C)c12
228.2
E-0010625
Cc1c2ccncc2cc2c3cc(OCC(=O)N(C)C)ccc3n(C)c12
867.9
E-0010627
Cc1nc(C(=O)NCCCN(C)C)cc2c3cc(OCCN4CCOCC4)ccc3n(C)c12
498.1
E-0010628
Cc1nc(C(=O)NCCCN(C)C)cc2c3cc(OCC4=NCCN4)ccc3n(C)c12
56.6
E-0010656
CCn1c2ccc(OCC3=NCCN3)cc2c2cc3cnccc3c(C)c21
485.1
E-0010660
Cc1c2ccncc2cc2c3cc(OCC4=NCCN4C)ccc3n(C)c12
244.4
E-0010662
Cn1c2ccc(OCCN3CCOCC3)cc2c2cc3cnccc3cc21
1,364.9
E-0010676
Cc1c2ccncc2cc2c1sc1ccc(OCCN3CCOCC3)cc12
2,336.6
E-0010686
Cc1c2ccncc2cc2c3cc(OCC4=NCCN4)ccc3n(C(C)C)c12
405.1
E-0010690
Cc1c2ccncc2cc2c3cc(C(N)=O)ccc3n(C)c12
1,405.5
E-0010691
CNC(=O)c1ccc2c(c1)c1cc3cnccc3c(C)c1n2C
221.8
E-0010712
Cc1c2ccncc2cc2c3cc(C(=O)NCCO)ccc3n(C)c12
191.4
E-0010713
COCCNC(=O)c1ccc2c(c1)c1cc3cnccc3c(C)c1n2C
1,900.2
E-0010724
Cc1c2ccncc2cc2c3cc(OC[C@H](C)N)ccc3n(C)c12
71.1
E-0010727
Cc1nc(C(N)=O)cc2c3cc(OCCN4CCOCC4)ccc3n(C)c12
1,602.1
E-0010730
Cc1c2ccnc(C(=O)N[C@H](C)CN(C)C)c2cc2c3ccc(OCCCN4CCN(C)CC4)cc3n(C)c12
1,050.6
E-0010733
Cn1c2ccc(OCCN3CCOCC3)cc2c2cc3ncccc3cc21
3,579
E-0010734
CN(C)CCOc1ccc2c(c1)c1cc3ncccc3cc1n2C
1,959.7
E-0010736
Cc1c2ccncc2cc2c3cc(OCCN4CCC(O)CC4)ccc3n(C)c12
177.3
E-0010737
Cc1c2ccncc2cc2c3cc(OCCN4CCC(F)CC4)ccc3n(C)c12
545.1
E-0010738
Cc1c2ccncc2cc2c3cc(OC[C@H](C)N(C)C)ccc3n(C)c12
260.7
E-0010739
Cc1c2c(cc3c4cc(OCC5=NCCN5)ccc4n(C)c13)C(=O)NCC2
175.5
E-0010751
Cn1c2ccc(OCC3=NCCN3)cc2c2cc3cnccc3cc21
687.7
E-0010754
CN(C)CCOc1ccc2c(c1)c1cc3cnccc3cc1n2C
369.4
E-0010765
Cc1c2ccnc(C(=O)N[C@H](C)CN(C)C)c2cc2c3c(OCCCN4CCN(C)CC4)cccc3n(C)c12
122.1
E-0010766
Cc1nc(C(N)=O)cc2c3cc(OCC4=NCCN4)ccc3n(C)c12
244.7
E-0010767
Cn1c2ccc(OCCN3CCCCC3)cc2c2nc3cnccc3nc21
448.1
E-0010768
Cc1c2ccncc2cc2c3cc(C#N)ccc3n(C)c12
899.7
E-0010771
CN(C)CCNC(=O)c1ccc2c(c1)c1cc3ccncc3nc1n2C
739.2
E-0010772
Cn1c2ccc(OCC3=NCCN3)cc2c2cc3ccncc3nc21
1,145.2
E-0010774
Cc1c2ccncc2cc2c3cc(N4CCN(C)CC4)ccc3n(C)c12
299.1
E-0010776
Cn1c2ccc(OCCN3CCOCC3)cc2c2nc3cnccc3nc21
1,125.8
E-0010784
Cn1c2ccc(OCC(=N)N)cc2c2cc3cnccc3cc21
683
E-0010786
Cn1c2ccc(OCC3=NCCN3)cc2c2nc3cnccc3nc21
548.8
E-0010812
Cc1c2c(cc3c4cc(OCC5=NCCN5)ccc4n(C)c13)C(=O)N(C)CC2
439.1
E-0010816
CN(C)CCCNc1nccc2c1C(=O)c1c([nH]c3ccc(OCC4=NCCN4)cc13)C2=O
34
E-0010922
Cc1c2ccncc2cc2c3cc(OCC4=NCCN4)cc(F)c3n(C)c12
1,129.6
E-0010975
CCn1c2ccc(OCC3=NCCN3)cc2c2cc3cnccc3cc21
273.9
E-0010989
Cc1c2ccncc2cc2c3cc(OCCN(C)C)cc(F)c3n(C)c12
799.6
E-0010994
Cn1c2ccc(OCC(=N)N)cc2c2cc3ncccc3cc21
1,853.6
E-0011026
COc1ccc(Nc2nccc(NCCc3ccncc3)n2)cc1
1,164.8
E-0011079
c1cc2cc3[nH]c4ccc(OCC5=NCCN5)cc4c3cc2cn1
193.3
E-0011083
CNc1ccnc(N(CCc2cccnc2)c2ccnc(Nc3ccc(Cl)cc3)n2)n1
339.3
E-0011122
NC(=O)CN1C(=O)Cc2ccc(-c3ccccc3)cc21
136.3
E-0011133
CNc1ccnc(N(CCc2cccnc2)c2ccnc(Nc3ccc(OC)cc3)n2)n1
319.3
E-0011137
CNc1ccnc(N(CCc2ccncc2)c2ccnc(Nc3ccc(OC)cc3)n2)n1
361.8
E-0011166
Cc1c2cnccc2cc2c3cc(OCCN(C)C)ccc3n(C)c12
664.2
E-0011190
Cc1c2ccncc2cc2c3cc(OCC[C@@H](C)N)ccc3n(C)c12
193.9
E-0011191
COc1ccc2c(c1)c1cc(NC(=O)NCCN(C)C)nc(C)c1n2C
304.7
E-0011195
NC(=O)CCN1C(=O)Cc2c1ccc1ccccc21
379
E-0011210
Cc1c2ccncc2cc2c3cc(OCC[C@H](C)N)ccc3n(C)c12
236.2
E-0011211
Cc1c2ccncc2cc2c3cc(OC[C@H](C)Nc4ccccn4)ccc3n(C)c12
1,513.3
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ExpansionRx-OpenADMET MLM CLint

MLM CLint dataset from the ExpansionRx-OpenADMET Blind Challenge [1] [2]. It is intended to be used through scikit-fingerprints library.

The task is to predict MLM CLint of molecules.

Characteristic Description
Tasks 1
Task type regression
Total samples 5692
Recommended split time
Recommended metric MAE

References

[1] OpenADMET team "Announcement 1: ExpansionRx-OpenADMET Blind Challenge" https://openadmet.ghost.io/expansionrx-openadmet-blind-challenge/

[2] HuggingFace Hub - ExpansionRx-OpenADMET challenge full dataset https://huggingface.co/datasets/openadmet/openadmet-expansionrx-challenge-data
Downloads last month
60
Size of downloaded dataset files:
380 kB
Size of the auto-converted Parquet files:
167 kB
Number of rows:
5,692