VS Results

	A	B	C	D	E	F	G	H	I	J	K	L	M	N	O	P
1			MMV000858	MMV001059	MMV006239	MMV020081	MMV020136	MMV020391	MMV020520	MMV020623	MMV020710	MMV085210	MMV688980
2															Mean
3	semi-supervised machine learning, used to construct QSAR models. Molecules were featurised by either Graph convolutional techniques or with 1024 Bit ECFP4 descriptors.	Davy Guan EC50 Model	107	343	400	78	236	258	371	366	290	206	362	Davy Guan EC50 Model	274
4		Davy Guan EC50 Model - flipped	294	58	1	323	165	143	30	35	111	195	39	Davy Guan EC50 Model - flipped	127	I've flipped the rankings of the EC50 model as I think they are in the wrong order
5		Davy Guan Model Replicate 3	394	345	1	93	263	65	173	198	108	298	223	Davy Guan Model Replicate 3	196
6		Davy Guan Replicate 1	388	287	3	165	254	131	334	349	13	341	120	Davy Guan Replicate 1	217
7		Davy Guan Replicate 2	280	118	3	317	104	27	368	388	36	188	248	Davy Guan Replicate 2	189
8	PfATP4 Ion Regulation Activity classification model using machine learning	Giovanni Cincilla	388	286	1	207	277	100	17	274	50	357	94	Giovanni Cincilla	186
9	Random forest with molecular fingerprints	James McCulloch	328	330	3	19	383	84	11	68	23	247	191	James McCulloch	153
10	Gradient boosting model (using xgboost) to predict actives and nonactives	Johnathan Silva	385	396	22	208	294	94	181	41	49	323	107	Johnathan Silva	191
11	library of 'common' transformations' as seen in CHEMBL	Vito Spadavecchio	153	318	1	63	384	387	392	84	351	327	264	Vito Spadavecchio	248
12	QSAR model based on homology modeling of PfATP4 -Cresset Forge	Ho Leung Ng	106	64	180	77	99	57	18	22	115	93	11	Ho Leung Ng	77
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14					KAE678 NITD678 - in training set, Cl rather than Br
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