https://www.molgen.de/online.html EXAMPLE 5: Generate all (theoretically possible) structures of mass ≤ 40 with elements, C, H, N3, O Hu, Stumpfe, Bajorath, J Med Chem (2016) https://doi.org/10.1021/acs.jmedchem.5b01746 Liu, Naderi, Alvin, Mukhopadhyay, Brylinski, JCIM (2017) https://doi.org/10.1021/acs.jcim.6b00596
H H 3 C N 0.739 ꣖㹱慬䏝 H 3 C H 3 C NH O N O N O CH3 O N NH 2 O CH3 Br CH3 N H 3 C H N S N O CH3 N OH CH3 CH3 N N N CH3 H 3 C H2 N NH2 Ⰵ⳿⸂ך 鋅劤⢽ 堣唒㷕统ַכֲֿ鋅ִָ˘ ݟຊྫ͔ΒͷϓϩάϥϜੜ
• Endocrine disruption • Growth inhibition • Aqueous solubility N NH O O H H H H H H H H H H H H H H H H H H H H H H H H H O O O O O O Cl H H H H H H H H H H H H H H H H H Br Br O P O O Br Br O Br Br H H H H H H H H H H H H H H H N S N N H H H H H H H H H H H H H H H O N O O H H H O O H H N O O Cl Cl Cl H H H H H H H N O O H H H H H H H H H N O O H H H H H H H N H N O O N O O H H H H H H H H N CH3 O O H N Cl Cl Cl Cl Cl H 3 C O O O O O O H 3 C CH3 CH2 O HN O O NH CH3 HO OH CH 3 N O O CH 3 N N H N H H 3 C N H 3 C H 3 C NH O N O N O CH3 O N NH 2 O CH3 Br CH3 N H 3 C H N S N O CH3 N OH CH3 CH3 N N N CH3 H 3 C H2 N NH2 H OH O HO CH 3 H H O CH 3 H O O H 3 C H H H O H 3 C S CH3 O H H O CH3 CH3 O O HO H 3 C H HO F H O H 3 C NH 2 O N HO H O O H H O O O H 3 C O O O CH 3 O CH 3 H O CH 3 H O O CH 3 H H N H N O H 3 C O O O
C H H H H H N O C C C C H H H H H ぐ暴䗙كؙزٕך刿倜 .-1זו (sum, mean or max) 갫殢װ侧ח⣛㶷׃זְ꧊秈乼⡲ .-1זו Ԯ ԯ Ԯ ԯ 縧䳔♶㢌 JOWBSJBOU + attention 縧䳔ず㢌 FRVJWBSJBOU <latexit sha1_base64="WNEpfX6Bt3G9f3Toyi7bd0iGAgY=">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</latexit> hi 0 @hi, M j2Ni (hi, hi, eij) 1 A
H H H H H Dipole moment Isotropic polarizability HOMO energy LUMO energy Gap between HOMO and LUMO Electronic spatial extent Zero point vibrational energy Internal energy at 0K Internal energy at 298.15K Enthalpy at 298.15K Free energy at 298.15K Heat capavity at 298.15K Atomization energy at 0K Atomization energy at 298.15K Atomization enthalpy at 298.15K Atomization free energy at 298.15K Rotational constant A Rotational constant B Rotational constant C ICML 2017 https://arxiv.org/abs/1704.01212 JCTC 2017 https://doi.org/10.1021/acs.jctc.7b00577 (// ꆀ㶨⻉㷕鎘皾 㺘䏝害ꟼ侧岀 %'5 鎘皾 椚锷ח㛇בֻ怴糊涸זءىُٖ٦ءّٝ鎘皾堣唒㷕统ד➿椚ׇׁص٦ؤ 暴חⴓ㶨⹛⸂㷕鎘皾 .%鎘皾 זוך欽鷿ד넝礵䏝ז⾱㶨هذٝءָٍٕק׃ְ ♷ִ⾱㶨ך瑞⡘縧ַ⾱㶨禸ךهذٝءٍٕؒطؘٕ٦鎘皾ׅꟼ侧
neighbors who are “heavy” (non-hydrogen) atoms • the valence minus the number of hydrogens • the atomic number • the atomic mass • the atomic charge • the number of attached hydrogens • whether the atom is contained in at least one ring %BZMJHIU ⾱㶨♶㢌ꆀ • hydrogen-bond acceptor or not? • hydrogen-bond donor or not? • negatively ionizable or not? • positively ionizable or not? • aromatic or not? • halogen or not? 䎗⡦涸(//ד⢪ⴓ㶨邌植 Rogers and Hahn, JCIM (2005) https://doi.org/10.1021/ci100050t Faber et al, JCTC (2017) https://doi.org/10.1021/acs.jctc.7b00577 .1//ח״ꆀ㶨⻉㷕鎘皾鵚⡂ד欽ְ갥挿٥鴟暴䗙 鸬竲ꆀٓكٕ
2021 • Self-Supervised Graph Transformer on Large-Scale Molecular Data • RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist • Reinforced Molecular Optimization with Neighborhood- Controlled Grammars • Autofocused Oracles for Model-based Design • Barking Up the Right Tree: an Approach to Search over Molecule Synthesis DAGs • On the Equivalence of Molecular Graph Convolution and Molecular Wave Function with Poor Basis Set • CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models • A Graph to Graphs Framework for Retrosynthesis Prediction • Hierarchical Generation of Molecular Graphs using Structural Motifs • Learning to Navigate in Synthetically Accessible Chemical Space Using Reinforcement Learning • Reinforcement Learning for Molecular Design Guided by Quantum Mechanics • Multi-Objective Molecule Generation using Interpretable Substructures • Improving Molecular Design by Stochastic Iterative Target Augmentation • A Generative Model for Molecular Distance Geometry • GraphDF: A Discrete Flow Model for Molecular Graph Generation • An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming • Equivariant message passing for the prediction of tensorial properties and molecular spectra • Learning Gradient Fields for Molecular Conformation Generation • Self-Improved Retrosynthetic Planning • Directional Message Passing for Molecular Graphs • GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation • Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space • A Fair Comparison of Graph Neural Networks for Graph Classification • MARS: Markov Molecular Sampling for Multi-objective Drug Discovery • Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design • Learning Neural Generative Dynamics for Molecular Conformation Generation • Conformation-Guided Molecular Representation with Hamiltonian Neural Networks • Symmetry-Aware Actor-Critic for 3D Molecular Design 堣唒㷕统ⴓꅿךمحززؾحؙד֮
itakigawa
eumesy
kinoue_smarthr
fkske
chck
kzinmr
nttcom
e869120_sub
1manken
icttitech
shimacos
koide3
murakawatakuya
retrage
samanthasiow
geoffreycrofte
panda_program
codingconduct
katarinadahlin
joshbly
ipullrank
eileencodes
geshan
moore
![/ 41 1 ⴓ㶨ךؚٓؿ邌植ה堣唒㷕统ך剑鵚 戣䊛♧㷕 [email protected] 椚⻉㷕灇瑔䨽ꬠ倜濼腉窟さ灇瑔إٝة٦ J14稢脄鸬䵿⼔㷕涸ٔأؙ㔐鼘ث٦ي 痥33㔐椚灇AIP Open](https://files.speakerdeck.com/presentations/9980309c6c4f42929c347cc457b700c6/slide_0.jpg){kind=link}
