doi stringlengths 10 10 | chunk-id int64 0 936 | chunk stringlengths 401 2.02k | id stringlengths 12 14 | title stringlengths 8 162 | summary stringlengths 228 1.92k | source stringlengths 31 31 | authors stringlengths 7 6.97k | categories stringlengths 5 107 | comment stringlengths 4 398 ⌀ | journal_ref stringlengths 8 194 ⌀ | primary_category stringlengths 5 17 | published stringlengths 8 8 | updated stringlengths 8 8 | references list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1502.02072 | 50 | Dataset pcba-aid602310 pcba-aid602313 pcba-aid602332 pcba-aid624170 pcba-aid624171 pcba-aid624173 pcba-aid624202 pcba-aid624246 pcba-aid624287 pcba-aid624288 pcba-aid624291 pcba-aid624296* pcba-aid624297* pcba-aid624417 pcba-aid651635 pcba-aid651644 pcba-aid651768 pcba-aid651965 pcba-aid652025 pcba-aid652104 pcba-aid65... | 1502.02072#50 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 51 | 773 404 440 402 621 406 224 372 045 367 273 334 388 336 077 345 619 333 378 336 050 398 731 387 779 361 115 362 320 331 953 364 365 396 566 324 774 368 281 358 501 354 086 368 048 353 881 14 532 363 349 path- path- path- Target Vif-A3G Vif-A3F GRP78 GLS Nrf2 PYK BRCA1 ERG Gsgsp Gsgsp a7 DNA re-replication DNA re-replic... | 1502.02072#51 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 52 | protein-protein interaction protein-protein interaction promoter other enzyme transcription fac- tor other enzyme promoter miscellaneous signalling way signalling way promoter miscellaneous miscellaneous GPCR promoter miscellaneous other enzyme protease signalling way miscellaneous other enzyme miscellaneous viability ... | 1502.02072#52 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 54 | Dataset muv-aid692 muv-aid712* muv-aid713* muv-aid733 muv-aid737* muv-aid810* muv-aid832 muv-aid846 muv-aid852 muv-aid858 muv-aid859 tox-NR-AhR tox-NR-AR-LBD* tox-NR-AR* tox-NR-Aromatase tox-NR-ER-LBD* tox-NR-ER* tox-NR-PPAR-gamma* tox-SR-ARE tox-SR-ATAD5 tox-SR-HSE tox-SR-MMP tox-SR-p53 dude-aa2ar dude-abl1 dude-ace d... | 1502.02072#54 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 55 | 15 000 14 997 15 000 15 000 14 999 14 999 15 000 15 000 15 000 14 999 15 000 5780 6520 6955 5521 6604 5399 6263 4889 6807 6094 4891 6351 31 546 10 749 16 899 26 240 5450 35 900 15 848 14 997 16 441 transcription fac- tor miscellaneous protein-protein interaction protein-protein interaction protein-protein interaction p... | 1502.02072#55 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 56 | ARE ATAD5 HSE mitochondrial membrane potential p53 signalling Adenosine A2a receptor Tyrosine-protein kinase ABL Angiotensin-converting enzyme Acetylcholinesterase Adenosine deaminase ADAM17 Beta-1 adrenergic receptor Beta-2 adrenergic receptor Serine/threonine-protein AKT Serine/threonine-protein AKT2 Aldose reductase... | 1502.02072#56 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 57 | Dataset dude-braf dude-cah2 dude-casp3 dude-cdk2 dude-comt dude-cp2c9 dude-cp3a4 dude-csf1r dude-cxcr4 dude-def dude-dhi1 dude-dpp4 dude-drd3 dude-dyr dude-egfr dude-esr1* dude-esr2 dude-fa10 dude-fa7 dude-fabp4 dude-fak1* dude-fgfr1 dude-fkb1a dude-fnta dude-fpps dude-gcr dude-glcm* dude-gria2 dude-grik1 dude-hdac2 du... | 1502.02072#57 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 58 | 19 350 GPCR other enzyme other enzyme 533 480 231 542 40 943 34 037 17 192 35 047 protease GPCR other enzyme other receptor 383 367 537 114 47 20 675 20 190 28 315 6250 2750 transcription fac- tor transcription fac- tor protease protease miscellaneous 100 139 111 592 5350 8697 5800 51 481 protein kinase other receptor ... | 1502.02072#58 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 59 | IV Dopamine D3 receptor Dihydrofolate reductase Epidermal growth factor receptor erbB1 Estrogen receptor alpha Estrogen receptor beta Coagulation factor X Coagulation factor VII Fatty binding acid adipocyte FAK Fibroblast growth factor receptor 1 FK506-binding protein 1A Protein ferase/geranylgeranyltransferase type I ... | 1502.02072#59 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 60 | glucocerebrosidase Glutamate receptor ionotropic Glutamate kainate 1 Histone deacetylase 2 Histone deacetylase 8 Human immunodeï¬ciency virus type 1 integrase Human immunodeï¬ciency virus type 1 protease Human immunodeï¬ciency virus type 1 reverse transcriptase HMG-CoA reductase HSP90 Hexokinase type IV
338
18 891
#... | 1502.02072#60 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 61 | Dataset dude-igf1r dude-inha dude-ital dude-jak2 dude-kif11 dude-kit dude-kith dude-kpcb dude-lck dude-lkha4 dude-mapk2 dude-mcr dude-met dude-mk01 dude-mk10 dude-mk14 dude-mmp13 dude-mp2k1 dude-nos1 dude-nram dude-pa2ga dude-parp1 dude-pde5a dude-pgh1 dude-pgh2 dude-plk1 dude-pnph dude-ppara dude-ppard dude-pparg* dud... | 1502.02072#61 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 62 | 35 848 37 195 8149 transcription fac- tor other receptor protein kinase protein kinase protein kinase protease protein kinase 100 98 99 508 398 195 435 107 8048 6199 5150 30 049 27 547 10 800 23 149 6800 other enzyme other enzyme other enzyme other enzyme other enzyme other enzyme other enzyme protein kinase 103 373 24... | 1502.02072#62 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 64 | other enzyme transcription fac- tor transcription fac- tor transcription fac- tor transcription fac- tor other enzyme other enzyme other enzyme other enzyme protease protein kinase transcription fac- tor other enzyme protein kinase
# dude-ptn1 dude-pur2 dude-pygm dude-pyrd dude-reni dude-rock1 dude-rxra
130 50 77 111 1... | 1502.02072#64 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 65 | 63 524
3450 34 491
# Adenosylhomocysteinase Tyrosine-protein kinase SRC
Massively Multitask Networks for Drug Discovery
Dataset dude-tgfr1 dude-thb dude-thrb dude-try1 dude-tryb1 dude-tysy dude-urok dude-vgfr2 dude-wee1 dude-xiap Actives Inactives Target Class Target 133 103 461 449 148 109 162 409 102 8500 7448 26 999... | 1502.02072#65 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 66 | Massively Multitask Networks for Drug Discovery
Count [a DUD-E Mi Tox21 ME MUV Ma ~PCBA 40 30 20 : a = 0 a a | | | oo ce ce ot ZB os Oy oo es oF oi © yar gs* eo xe ee * gg aw a xo Ho gh ja? on on yo® oe ® g o fe) a? s ont 38 io Target Class
Figure A.1. Target class breakdown. Classes with fewer than ï¬ve members we... | 1502.02072#66 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 67 | Massively Multitask Networks for Drug Discovery
Table A.3. Held-in datasets.
Dataset pcba-aid899 pcba-aid485297 pcba-aid651644 pcba-aid651768 pcba-aid743266 muv-aid466 muv-aid852 muv-aid859 tox-NR-Aromatase tox-SR-MMP Actives Inactives Target Class Target 1809 9126 748 1677 306 30 30 30 300 919 7575 311 481 361 115 362... | 1502.02072#67 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 68 | Table A.4. Held-out datasets.
Dataset pcba-aid1461 pcba-aid2675 pcba-aid602233 pcba-aid624417 pcba-aid652106 muv-aid548 muv-aid832 muv-aid846 tox-NR-AhR tox-SR-ATAD5 Actives Inactives Target Class Target 2305 99 165 6388 496 30 30 30 768 264 218 561 279 333 380 904 398 731 368 281 15 000 15 000 15 000 5780 6807 NPSR GP... | 1502.02072#68 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 69 | Massively Multitask Networks for Drug Discovery
2.0 1.5 + 1.0 0.5 0.0 A Log-odds-mean-AUC Duplicate Unique
Figure A.3. Multitask performance of duplicate and unique targets. Outliers are omitted for clarity. Notches indicate a conï¬dence interval around the median, computed as ±1.57 à IQR/
Massively Multitask Networ... | 1502.02072#69 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 70 | Logistic Regression (LR) Random Forest (RF) Single-Task Neural Net (STNN) Pyramidal (2000, 100) STNN, .25 Dropout (PSTNN) Max{LR, RF, STNN, PSTNN} 1-Hidden (1200) Layer Multitask Neural Net (MTNN)
Table B.2. Enrichment scores for all models reported in Table 2. Each value is the median across the datasets in a group of... | 1502.02072#70 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 71 | Model MUV 0.5% 1% 2% 5% 0.5% 1% 2% 5% 0.5% 1% 2% 5% PCBA Tox21 19.4 LR 40.0 RF 19.0 STNN 21.8 PSTNN MTNN 33.8 PMTNN 43.8 16.5 27.4 15.6 16.9 23.6 29.6 12.1 17.4 11.8 12.4 16.9 19.7 7.9 9.1 7.7 7.9 9.8 11.2 20.0 40.0 26.7 26.7 26.7 40.0 23.3 26.7 20.0 16.7 16.7 23.3 15.0 16.7 11.7 13.3 16.7 16.7 8.0 7.3 8.0 8.0 8.7 10.0... | 1502.02072#71 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 72 | Massively Multitask Networks for Drug Discovery
40 PCBA 0.9 0.8 0.7 0.6 + 05 8 } i : : â | 0.4 MUV ¢ 10 a) = 09 oO to) © 08 3 2s 0.7 fe) . (5 06 & $05 oO = 04 40 Tox21 0.9 O7 t zk qT 06 7 i 0.5 0.4 gw « \) eats ys N) \ @ ow x oe? oo . Rw ao we P n ® a soeâ Wenâ >».
Figure B.1. Graphical representation of dat... | 1502.02072#72 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 73 | Massively Multitask Networks for Drug Discovery
# C. Training Details
The multitask networks in Table 2 were trained with learning rate .0003 and batch size 128 for 50M steps using stochastic gradient descent. Weights were initialized from a zero-mean Gaussian with standard deviation .01. The bias was initialized at .5... | 1502.02072#73 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 74 | As we noted in the main text, the datasets in our collection contained many more inactive than active compounds. To ensure the actives were given adequate importance during training, we weighted the actives for each dataset to have total weight equal to the number of inactives for that dataset (inactives were given uni... | 1502.02072#74 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 75 | Model PCBA (n = 128) MUV (n = 17) Tox21 (n = 12) Pyramidal (1000, 50) MTNN Pyramidal (1000, 100) MTNN Pyramidal (1000, 150) MTNN Pyramidal (2000, 50) MTNN Pyramidal (2000, 100) MTNN Pyramidal (2000, 150) MTNN Pyramidal (3000, 50) MTNN Pyramidal (3000, 100) MTNN Pyramidal (3000, 150) MTNN .846 .845 .842 .846 .846 .845 .... | 1502.02072#75 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 76 | A 8-Hidden (300) Layer MTNN, auxiliary heads attached to hidden layers 3 and 6, 6M steps B 1-Hidden (3000) Layer MTNN, 1M steps C 1-Hidden (3000) Layer MTNN, 1.5M steps D Pyramidal (1800, 100), 2 deep, reconnected (original input concatenated to ï¬rst pyramid output) E Pyramidal (1800, 100), 3 deep F G Pyramidal (2000... | 1502.02072#76 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 77 | Model PCBA (n = 128) MUV (n = 17) Tox21 (n = 12) Sign Test CI Paired t-Test A B C D E F G H I J K L .836 .835 .837 .842 .842 .858 .831 .856 .860 .830 .859 .872 .793 .855 .851 .842 .808 .836 .795 .827 .862 .810 .843 .837 .786 .769 .765 .816 .789 .810 .774 .796 .824 .801 .803 .802 [.01, .06] [.11, .22] [.12, .24] [.08, .... | 1502.02072#77 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
1502.02072 | 79 | Massively Multitask Networks for Drug Discovery
# References
Jain, Ajay N and Nicholls, Anthony. Recommendations for evaluation of computational methods. Journal of computer- aided molecular design, 22(3-4):133â139, 2008.
McGill, Robert, Tukey, John W, and Larsen, Wayne A. Variations of box plots. The American Statis... | 1502.02072#79 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 2... | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] |
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