Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
コンペティションから見るAI創薬/AI drug discovery in the view of competitions
Search
m_mochizuki
March 18, 2019
Research
2
1.4k
コンペティションから見るAI創薬/AI drug discovery in the view of competitions
日本オミックス医学会シンポジウム 発表資料
場所: 東京医科歯科大学
日付: 2019/3/18
2018/3/20 誤記修正
2018/3/21 誤記修正
m_mochizuki
March 18, 2019
Tweet
Share
More Decks by m_mochizuki
See All by m_mochizuki
SIGNATE: 日本取引所グループ ファンダメンタルズ分析チャレンジ 1位解法 / the 1st place solution of JPX Fundamentals Analysis Challenge on SIGNATE
m_mochizuki
4
7.4k
SIGNATE: 日本取引所グループ ファンダメンタルズ分析チャレンジ 暫定1位解法 / the provisional 1st place solution of JPX Fundamentals Analysis Challenge on SIGNATE
m_mochizuki
3
8.6k
MD-DSC研究会講演資料:『機械学習コンペティションの実際とその意義』/ Practice on ML competition and its significance
m_mochizuki
1
1k
Other Decks in Research
See All in Research
The Future of AI: Beyond Completion Models to Systematic Innovation
sunghopark0
0
120
自然言語とVision&Language
kuehara
19
4.4k
ICLR2024 LLMエージェントの研究動向
masatoto
13
9.1k
SSII2024 [OS2] 画像、その先へ 〜モーション解析への誘い〜
ssii
PRO
1
1.1k
Introduction of NII S. Koyama's Lab (AY2024)
skoyamalab
0
330
新入生向けチュートリアル:文献のサーベイv2
a1da4
9
7.8k
20240710_熊本県議会・熊本市議会_都市交通勉強会
trafficbrain
0
560
AIが非ヒト動物に与える有益・有害な影響の検討
takeshit_m
0
290
CARA MEMBUKA VIDEO DEWASA DI INDONESIA
bloglangit
0
320
ランサーズエージェント_フリーランスエンジニアの年収・キャリアの実態調査2024
lancers_pr
0
310
LayerXにおけるAI・機械学習技術の活用と展望 / layerx-ai-jsai2024
shimacos
2
2.5k
高精度、高効率アナログCompute-in-Memory回路に向けて
kentaroy47
2
100
Featured
See All Featured
Clear Off the Table
cherdarchuk
89
320k
Learning to Love Humans: Emotional Interface Design
aarron
269
39k
GraphQLとの向き合い方2022年版
quramy
36
13k
Optimizing for Happiness
mojombo
373
69k
Why Our Code Smells
bkeepers
PRO
332
56k
Git: the NoSQL Database
bkeepers
PRO
423
64k
Into the Great Unknown - MozCon
thekraken
20
1.3k
Docker and Python
trallard
37
2.9k
Designing for humans not robots
tammielis
247
25k
Leading Effective Engineering Teams 2024
addyosmani
3
300
Faster Mobile Websites
deanohume
303
30k
Creatively Recalculating Your Daily Design Routine
revolveconf
214
11k
Transcript
a I ( M A M89 :) 3 /21 1
/ 0/ ) ) A c
s ( 21 1- 40 z ( u 76 6
7 ) ) h o 7M M AI( : 7 r a ( k c 7 i
3 IS o J P 123 32 0 J P
T 0 J P T 0 J P T 6 n 0 J P T 4 DJ PG C A 7E5 D4J A C C eB E5
P GI gGI M ci 06?5:
0 65A 4? 5 B C 84 6 76 .?4 8 11ae h M ci 2 3 gM N M ci g T M
6 AI= +.9<*!%;7BG =?4D +.1:/"(&#*
5C0 A, "(&$ = %&' )6F5C=? >@8E3 -23
<; .%7 FC*
'4 <;p53L. + '&(AE!1IBM) DK5=(FC*'40 BH1G .%7D/:36I (-"8$) .%7>@1G JD ↑ # 2?, )9
1
1 0 0 n RAK ) S2 AK K n
g 1 0 Ra Ra 0 5 Q e ( 425 %10/7& (! * , 10/(! $%)+.# # " 425 'Kaggle(063(-
1 n eh a kGn f R / :/ V
Fod eh aFH lm g Sb RHMS A n V . :/ i eh a cS A p 2 / /: / ./: :.
M ,1 42 , 2 , 0 0 9 22
3. n ? n : AC
None
4 1 #)$ ' !%( * $
(Convolutional Neural Network) $ %" & &
1 $)& (" Fingerprint/Descriptor % & (Graph
Convolutional Neural Network) & ! # ' '
n 1 G 6 C n N : Altae-Tran et
al, ACS Cent. Sci.,2017,3(4), pp 283–293
n ( ) P O N B A n I
24 0 2 1 1 Virtual screening… 1 2 3
n N G n K C 967 ( #
&).0 *! "%*@ =1;=>* *(400) 4?:<A5).0 “”* */$, 862?3' +(-OK
7 8 T d fng ] N mi a M
hmi ] C [ Ct ep r a 21 . ? 0 ?9 5 ( ) ) ( )( 02 s y , 9 T u CG ] NaY I
( 7 2 Extended Connectivity Fingerprint Functional Connectivity Fingerprint Topological
Torsions Atom Pairs Fingerprint RDKit Fingerprint Avalon fingerprint !fingerprint (6) 70 Random Forest Extremely Randomized Trees Gradient Boosted Trees Multilayer perceptron Support Vector Regression ! $)%(&' (5) 65 = 30# Elastic Net Pfinal Level 0 Level 1 " # 1 2 ) ):
Fingerprint ECFP FCFP TT AP RDK AVLN F-Stacking RF
0.848 0.855 0.816 0.686 0.652 0.722 0.892 ERT 0.869 0.889 0.844 0.798 0.671 0.768 0.907 GBT 0.852 0.864 0.835 0.808 0.733 0.758 0.891 MLP 0.802 0.777 0.623 0.814 0.651 0.712 0.895 SVR 0.856 0.852 0.688 0.763 0.662 0.693 0.877 L-Stacking 0.890 0.911 0.870 0.881 0.799 0.846 0.930 FL-Stacking Level0 ROC-AUC ) 1 ) (0( 72 n 0 3 0 0
2 ( 7 )1)0 3 5
IMSBIO () ( ) 1 8 1 Univ-shizuoka 1 PFDrug ()Preferred Networks 1 kiharalab 1 1 Graph CNN
1 0
38 5 120 n 0 u ”Taklbe : () :.
00 1Tcn n s T n p w Th cn dg I I “ L ing n r P v T ng D n T t p o y 2 8: : .2 1 2 - // /: 0:
)0 3 6 ( 2 1 n Lel an f
LN b i - b i -/) - n U N gc d - s n D - b i ( 1) -1- P t ( 1) ) vo el an (
)0 37 ( 2 1 n 24 9: 9 0
4 n 1 9 9 56 8 2 3 W 2 9 4 O O O !!!
9 21 n e l ( g n K n
) a )
20 2 3 n 3 t 1 o r ru
2 a i 1 o ru ) 2 l1 ru 1es 2 1 r f K2 n (( h g g K2
1 3 n ( ) ) n : )
X 8 T 9 8. A T 9 8. A
T 9 8. A 0 3 T . 9? A 5 2
( • 9) : / 51) 5 • 1 55
5 2 1:023/ 5