1. Materials Informatics uses Python tools like RDKit for analyzing molecular structures and properties.
2. ORGAN and MolGAN are two generative models that use GANs to generate novel molecular structures based on SMILES strings, with ORGAN incorporating reinforcement learning to optimize for desired properties.
3. Tools like RDKit enable analyzing molecular fingerprints and descriptors that can be used for machine learning applications in materials informatics.
ゼロから始める深層強化学習（NLP2018講演資料）/ Introduction of Deep Reinforcement LearningPreferred Networks
Introduction of Deep Reinforcement Learning, which was presented at domestic NLP conference.
言語処理学会第24回年次大会(NLP2018) での講演資料です。
http://www.anlp.jp/nlp2018/#tutorial
The importance of data curation on QSAR Modeling: PHYSPROP open data as a cas...Kamel Mansouri
This presentation highlighted how data curation impacts the reliability of QSAR models. We examined key datasets related to environmental endpoints to validate across chemical structure representations (e.g., mol file and SMILES) and identifiers (chemical names and registry numbers), and approaches to standardize data into QSAR-ready formats prior to modeling procedures. This allowed us to quantify and segregate data into quality categories. This improved our ability to evaluate the resulting models that can be developed from these data slices, and to quantify to what extent efforts developing high-quality datasets have the expected pay-off in terms of predicting performance. The most accurate models that we build will be accessible via our public-facing platform and will be used for screening and prioritizing chemicals for further testing.
Friday, October 15th, 2021, Sapporo, Hokkaido, Japan.
Hokkaido University ICReDD - Faculty of Medicine Joint Symposium
https://www.icredd.hokudai.ac.jp/event/5993
ICReDD (Institute for Chemical Reaction Design and Discovery)
https://www.icredd.hokudai.ac.jp
ゼロから始める深層強化学習（NLP2018講演資料）/ Introduction of Deep Reinforcement LearningPreferred Networks
Introduction of Deep Reinforcement Learning, which was presented at domestic NLP conference.
言語処理学会第24回年次大会(NLP2018) での講演資料です。
http://www.anlp.jp/nlp2018/#tutorial
The importance of data curation on QSAR Modeling: PHYSPROP open data as a cas...Kamel Mansouri
This presentation highlighted how data curation impacts the reliability of QSAR models. We examined key datasets related to environmental endpoints to validate across chemical structure representations (e.g., mol file and SMILES) and identifiers (chemical names and registry numbers), and approaches to standardize data into QSAR-ready formats prior to modeling procedures. This allowed us to quantify and segregate data into quality categories. This improved our ability to evaluate the resulting models that can be developed from these data slices, and to quantify to what extent efforts developing high-quality datasets have the expected pay-off in terms of predicting performance. The most accurate models that we build will be accessible via our public-facing platform and will be used for screening and prioritizing chemicals for further testing.
Friday, October 15th, 2021, Sapporo, Hokkaido, Japan.
Hokkaido University ICReDD - Faculty of Medicine Joint Symposium
https://www.icredd.hokudai.ac.jp/event/5993
ICReDD (Institute for Chemical Reaction Design and Discovery)
https://www.icredd.hokudai.ac.jp
BioAssay Express: Creating and exploiting assay metadataPhilip Cheung
The challenge of accurately characterizing bioassays is a real pain point for many drug discovery organizations. Research has shown that some organizations have legacy assay collections exceeding 20,000 protocols, the great majority of which are not accurately characterized. This problem is compounded by the fact that many new protocol registrations are still not following FAIR (Findability, Accessibility, Interoperability, and Reusability) Data principles.
BioAssay Express is a tool focused on transforming the traditional protocol description from an unstructured free form text into a well-curated data store based upon FAIR Data principles. By using well-defined annotations for assays, the tool enables precise ontology based searches without having to resort to imprecise keyword searches.
This talk explores a number of new important features designed to help scientists accelerate the drug discovery process. Some example use-cases include: enabling drug repositioning projects; improving SAR models; identifying appropriate machine learning data sets; fine-tuning integrative-omic pathways;
An aspirational goal for our team is to build a metadata schema based on semantic web vocabularies that is comprehensive to the extent that the text description becomes optional. One of the many possibilities is to take the initial prospective ELN entry for a bioassay protocol and feed it directly to an automated instrument. While there are many challenges involved in creating the ELN-to-robot loop, we will provide some insights into our collaborations with UCSF automation experts.
In summary, the ability to quickly and accurately search or analyze bioassay data (public or internal) is a rate limiting problem in drug discovery. We will present the latest developments toward removing this bottleneck.
https://plan.core-apps.com/acs_sd2019/abstract/6f58993d-a716-49ad-9b09-609edde5a3f4
Benchmarking Commercial RDF Stores with Publications Office DatasetGhislain Atemezing
The slides present a benchmark of RDF stores with real-world datasets and queries from the EU Publications Office (PO). The study compares the performance of four commercial triple stores: Stardog 4.3 EE, GraphDB 8.0.3 EE, Oracle 12.2c and Virtuoso 7.2.4.2 with respect to the following requirements: bulk loading, scalability, stability and query execution.
Introduction to Biological Network Analysis and Visualization with Cytoscape ...Keiichiro Ono
Introduction to biological network analysis and visualization with Cytoscape (using the latest version 3.4).
This is a first half of the lecture for Applied Bioinformatics lecture at TSRI.
Machine Learning for (DF)IR with Velociraptor: From Setting Expectations to a...Chris Hammerschmidt
achine Learning for DFIR with Velociraptor: From Setting Expectations to a Case Study
By Christian Hammerschmidt, PhD - Head of Engineering/ML, APTA Technologies
Machine learning (ML) or artificial intelligence (AI) often comes with great promise and large marketing budgets for cybersecurity, especially in monitoring (such as EDR/XDR solutions). Post-breach, it often turns out that the actual performance falls short of its promises.
In this talk, we’ll briefly look at ML for DFIR: What tasks can ML solve, generally speaking? What requirements do we have for a useful ML system in cybersecurity/DFIR contexts, such as reliability, robustness to attackers, and explainability? What makes ML difficult to apply in cybersecurity, e.g. when thinking about false alerts or attackers attempting to circumvent automated systems?
After discussing the basics, we look at ML for velociraptor:
How can we process forensic data collected with VQL using machine learning (with a typical Python/Jupyter/scikit-learn/PyTorch stack)?
And how can we build artifacts that run ML directly on each endpoint, avoiding central data collection?
The talk concludes with a case study, showing how we significantly reduced time to analyze EVTX files in incident response cases, saving thousands of USD in costs and reducing time to resolution.
Bio: Chris Hammerschmidt did his PhD research on machine learning methods for reverse engineering software systems. Now, he’s heading APTA Technologies, a start-up building machine learning tools to understand software behavior .
Affiliation: APTA Technologies, https://apta.tech
Metagenome is the entire genetic information of microorganism at specific site/time. Analysis of metagenomic data could be achieved by two approaches; 1) amplicon (16s RNA gene) data analysis and whole genome metagenomics data analysis. Here we focus on 16S rRNA amplicon using Mothur Pipeline for analysis of metagenomics data.
The EPA CompTox Chemistry Dashboard provides access to data associated with ~760,000 chemical substances. The available data includes experimental and predicted physicochemical properties, environmental fate and transport data, in vivo and in silico toxicity data, in vitro bioassay data, exposure data and a variety of other types of information. The data are under continuous expansion and curation and the experimental data have been used to develop QSAR and QSPR models. A number of these models are available via a web interface so that users can submit a chemical structure and predict properties in real time. The dashboard also provides access to pre-compiled chemical lists and categories, including pesticides, and chemicals detected in the environment via non-targeted mass spectrometry analysis. The data are searchable using chemical identifiers (systematic names, trade names, CAS Registry Numbers), by structure, mass and formula. Batch searches allow for data associated with thousands of chemicals to be obtained in a few seconds, with just a few button clicks, and downloaded to the desktop. This presentation will provide an overview of the Dashboard and its applications to accessing source data associated with agriculturally related chemicals. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.
Automating fetal heart monitor using machine learningTamjid Rayhan
This is a webinar held by the IEEE student branch of University of Chittagong. This talks about how a beginner can gain expert level knowledge in Machine learning and deep learning using online resources. It focuses on how the presentar solved a biomedical engineering problem using Machine learning. Also gives reference to many interesting references to advices given by the leaders of Machine learning field.
Presented at OECD Workshop on Systematic Reviews in the Scope of the Endocrine Disrupter Testing and Assessment (EDTA) Conceptual Framework Level 1 in Paris, France
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
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We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
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We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
19. RDKit
■
■
■
Getting Started with the RDKit in Python
https://www.rdkit.org/docs/
GettingStartedInPython.html
■RDKit
https://future-chem.com/rdkit-intro/
■
https://github.com/chemo-wakate
18
26. ■
25
1. 2. 3.
4.
5.
Molecular neural network models with RDKit and Keras in Python
http://www.wildcardconsulting.dk/useful-information/molecular-neural-
network-models-with-rdkit-and-keras-in-python/
Keras
http://www.ag.kagawa-u.ac.jp/charlesy/2017/07/21/
keras%E3%81%A7%E5%8C%96%E5%90%88%E7%89%A9%E3%81%AE%E6%BA%B6%E8%A7%
A3%E5%BA%A6%E4%BA%88%E6%B8%AC%EF%BC%88%E3%83%8B%E3%83%A5%E3%83%BC
%E3%83%A9%E3%83%AB%E3%83%8D%E3%83%83%E3%83%88%E3%83%AF%E3%83%BC/
39. SeqGAN
38
L.Yu, et al., AAAI2017.
https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/
14344/14489
https://github.com/LantaoYu/SeqGAN
(state): t-1
(action): t
40. ORGAN
■SeqGAN
39
s
d
,
d
-
h
d
o
e
)
.
t
,
Finally in SeqGAN the reward function is provided by D .
4 ORGAN
Figure 1: Schema for ORGAN. Left: D is trained as a classifier
receiving as input a mix of real data and generated data by G. Right:
G is trained by RL where the reward is a combination of D and the
objectives, and is passed back to the policy function via Monte Carlo
sampling. We penalize non-unique sequences.
Figure 1 illustrates the main idea of ORGAN. To take into
account domain-specific desired objectives Oi, we extend the
SeqGAN
SMILES
G.Guimaraes et al.(2017)
https://arxiv.org/abs/1705.10843
https://github.com/gablg1/ORGAN
41. ORGAN
40
Objective Algorithm Validity (%) Diversity Druglikeliness Synthesizability Solubility
MLE 75.9 0.64 0.48 (0%) 0.23 (0%) 0.30 (0%)
SeqGAN 80.3 0.61 0.49 (2%) 0.25 (6%) 0.31 (3%)
Druglikeliness ORGAN 88.2 0.55 0.52 (8%) 0.32 (38%) 0.35 (18%)
OR(W)GAN 85.0 0.95 0.60 (25%) 0.54 (130%) 0.47 (57%)
Naive RL 97.1 0.8 0.57 (19%) 0.53 (126%) 0.50 (67%)
Synthesizability ORGAN 96.5 0.92 0.51 (6%) 0.83 (255%) 0.45 (52%)
OR(W)GAN 97.6 1.00 0.20 (-59%) 0.75 (223%) 0.84 (184%)
Naive RL 97.7 0.96 0.52 (8%) 0.83 (256%) 0.46 (54%)
Solubility ORGAN 94.7 0.76 0.50 (4%) 0.63 (171%) 0.55 (85%)
OR(W)GAN 94.1 0.90 0.42 (-12%) 0.66 (185%) 0.54 (81%)
Naive RL 92.7 0.75 0.49 (3%) 0.70 (200%) 0.78 (162 %)
All/Alternated ORGAN 96.1 92.3 0.52 (9%) 0.71 (206%) 0.53 (79%)
ble 1: Evaluation of metrics, on several generative algorithms and optimized for different objectives for molecules. Reported values
an values of valid generated molecules. The percentage of improvement over the MLE baseline is reported in parenthesis. Values sho
bold indicate significant improvement. Shaded cell indicates direct optimized objectives.
ble 2 shows quantitative results comparing ORGAN to oth
baseline methods optimizing for three different metrics. O
GAN outperforms SeqGAN and MLE in all of the three m
rics. Naive RL achieves a higher score than ORGAN for
Ratio of Steps metric, but it under-performs in terms of
Druglikeliness, Synthesizability, Solubility
42. ORGANIC
■ORGAN
41
Methods
gure 1: Usage of ORGANIC illustrated. In the training procedure we show the thre
ndamental components: a generator, a discriminator, and a reinforcement metric. Arrow
icate the flow of inputs and outputs between networks.
B.S-.Lengeling, et al.(2017)
https://chemrxiv.org/articles/ORGANIC_1_pdf/5309668
https://github.com/aspuru-guzik-group/ORGANIC
43. MolGAN
■
■SMILES
42
ive model for small molecular graphs
Cao 1
Thomas Kipf 1
Molecular graph
Generator Discriminator
Reward
network
z ~ p(z)
0/1
0/1
x ~ pdata(x)
Generator Discriminator
N.D.Cao and T.Kipf(2018)
https://arxiv.org/abs/1805.11973
44. MolGAN
■
43
MolGAN: An implicit generative model for small molecular graphs
Generator
Graph
Molecule
N
N
N
N
N N
T T
z ~ p(z)
Adjacency tensor Sampled
SampledAnnotation matrix
~
~
GCN
GCN
0/1
0/1
Discriminator
Reward network
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Figure 2. Outline of MolGAN. From left: the generator takes a sample from a prior distribution and generates a dense adjacency tensor
A and an annotation matrix X. Subsequently, sparse and discrete ˜A and ˜X are obtained from A and X respectively via categorical
sampling. The combination of ˜A and ˜X represents an annotated molecular graph which corresponds to a specific chemical compound.
Finally, the graph is processed by both the discriminator and reward networks that are invariant to node order permutations and based on
Relational-GCN (Schlichtkrull et al., 2017) layers.
loss and the RL loss: passing them to D and ˆR in order to make the gen-
eration stochastic while still forwarding continuous ob-
N.D.Cao and T.Kipf(2018)
https://arxiv.org/abs/1805.11973
45. SMILES
■ SMILES
44
Grammar Variational Autoencoder
O
OH
'c1ccccc1'
smiles
chain
...
chain
branched
atom
atom
aromatic
organic
'c'
ringbond
digit
'1'
branched
atom
smiles chain
chain
branched
atom
chain
branched
atom
3
atom, ringbond
branched
atom
aromatic
organic
atom
'c'
aromatic
organic
2
ringbond digit
digit '1'
4 5
form parse tree extract rules convert to 1-hot vectors
input SMILES
map to latent space
6
chain,
...
......
...
chain branched atom
smiles chain
chain chain, branched atom
atom, ringbondbranched atom
atombranched atom
aromatic organicatom
aliphatic organicatom
ringbond digit
digit '1'
'c'aromatic organic
'C'aliphatic organic
'N'aliphatic organic
digit '2'
1
SMILES grammar
Figure 1. The encoder of the GVAE. We denote the start rule in blue and all rules that decode to terminal in green. See text for details.
tion rules. We describe how the GVAE works using a sim-
ple example.
Encoding. Consider a subset of the SMILES grammar as
shown in Figure 1, box 1 . These are the possible pro-
duction rules that can be used for constructing a molecule.
Imagine we are given as input the SMILES string for ben-
zene: ‘c1ccccc1’. Figure 1, box 2 shows this molecule.
To encode this molecule into a continuous latent represen-
tation we begin by using the SMILES grammar to parse this
string into a parse tree (partially shown in box 3 ). This
tree describes how ‘c1ccccc1’ is generated by the grammar.
We decompose this tree into a sequence of production rules
by performing a pre-order traversal on the branches of the
parse tree going from left-to-right, shown in box 4 . We
convert these rules into 1-hot indicator vectors, where each
dimension corresponds to a rule in the SMILES grammar,
box 5 . Letting K denote the total number of production
timesteps (production rules) allowed by the decoder. We
will use these vectors in the rest of the decoder to select
production rules.
To ensure that any sequence of production rules generated
from the decoder is valid, we keep track of the state of
the parsing using a last-in first-out (LIFO) stack. This is
shown in Figure 2, box 3 . At the beginning, every valid
parse from the grammar must start with the start symbol:
smiles, which is placed on the stack. Next we pop off
whatever non-terminal symbol that was placed last on the
stack (in this case smiles), and we use it to mask out the
invalid dimensions of the logit vector. Formally, for ev-
ery non-terminal ↵ we define a fixed binary mask vector
m↵ 2 [0, 1]K
. This takes the value ‘1’ for all indices in
1, . . . , K corresponding to production rules that have ↵ on
their left-hand-side.
In this case the only production rule in the grammar begin-
ning with smiles is the first so we zero-out every dimension
M.J.Kusner, et al. ICML2017
http://proceedings.mlr.press/v70/kusner17a
https://github.com/mkusner/grammarVAE
(variational autoencoder, VAE)
Grammar Variational Autoencoder
map from latent space
1 2
...
convert to logits
maxlength
smiles
chain
chain,
branched
atom
branched
atom
branched
atom,
atom,
branched
atomringbond,
aromatic
organic,
branched
atomringbond,
branched
atom
ringbond,
stack mask out invalid rules
pop first
non-terminal
sample rule &
push non-terminals
onto stack
chainsmiles
chain
branched
atom
chain,
chain
branched
atom
chain
smiles
chain
branched
atom
atom, ringbond
branched
atom
atom
aromatic
organic
ringbond
digit
branched
atom
atom
aromatic
organic
'c'
aromatic
organic
ringbond digit
digit '1'digit,
...
......
3 4 5
concatenate
terminals
6 'c1ccccc1'
7
translate
molecule
Figure 2. The decoder of the GVAE. See text for details.
Algorithm 1 Sampling from the decoder
Input: Deterministic decoder output F 2 RTmax⇥K
,
masks m↵ for each production rule ↵
Output: Sampled productions X from p(X|z)
1: Initialize empty stack S, and push the start symbol S
onto the top; set t = 0
2: while S is nonempty do
3: Pop the last-pushed non-terminal ↵ from the stack S
4: Use Eq. (2) to sample a production rule R
5: Let xt be the 1-hot vector corresponding to R
character-based VAE decoder is that at every point in the
generated sequence, the character VAE can sample any
possible character. There is no stack or masking opera-
tion. The grammar VAE however is constrained to select
syntactically-valid sequences.
Syntactic vs. semantic validity. It is important to note
that the grammar encodes syntactically valid molecules
but not necessarily semantically valid molecules. This is
mainly because of three reasons. First, certain molecules
46. SMILES
45
B
C
N
O
S
P
F
I
H
Cl
Br
1
2
3
(
)
[
]
B
C
N
O
S
P
F
I
H
Cl
Br
1
2
3
(
)
[
]
B
C
N
O
S
P
F
I
H
Cl
Br
1
2
3
(
)
[
]
B
C
N
O
S
P
F
I
H
Cl
Br
1
2
3
(
)
[
]
C C 1
y(x1|w) y(x2|x<2, w)
B
C
N
O
S
P
F
I
H
Cl
Br
1
2
3
(
)
[
]
C
y(x3|x<3, w) y(x4|x<4, w) y(x5|x<5, w)
(x1) (x2) (x3) (x4)
RNN
cell
sequence
inputs:
Figure 1: The recurren
imate the Q-function.
function activation is
acter in C. Here the
SMILES alphabet and
acters of the molecule
example. The initial
from the first hidden
continues until the en
during decoding, but its performance achieved by this method leaves scope fo
method requires hand-crafted grammatical rules for each application domain
In this paper, we propose a generative approach to modeling validity that
constraints of a given discrete space. We show how concepts from reinforce
used to define a suitable generative model and how this model can be approx
D.Janz, et al. ICLR2018
https://arxiv.org/abs/1712.01664
https://github.com/DavidJanz/molecule_grammar_rnn
LSTM
48. ■ AlphaGO
■
47
ARTICLE RESEARCH
and the first-degree neighbouring atoms. Only rules that occurred at
least 50 times in reactions published before 2015 were kept. For the
Prediction with the in-scope filter network
After the search space has been narrowed down by the expansion policy
Search tree representationChemical representation of the synthesis plana b
B
E
A
F
C D
A= {1} B= {2,6} C= {3,6}
D= {4,5,6} E= {8,9} F= {6,7,8}
Root (target)
Target
Terminal
solved state
N
O
CO2 Me
CO2Me
Boc
Ph
HN
O
CO2Me
CO2Me
Ph
MeCO2
MeO2C
1
2
3 5
4
6
7
9
8
N
Boc
Ph
OH
N
Boc
Ph
OTBS
HN
Ph
OH
N
H
Boc OTBS
Ph Br
+
+
+
+Boc2O
8
Boc2O
Figure 1 | Translation of the traditional chemists’ retrosynthetic route
representation to the search tree representation. a, The traditional
chemists’ retrosynthetic route representation (conditions omitted)50
.
b, The search tree representation. The nodes in the tree represent the
synthetic position, and contain all precursors needed to make the
molecules of the preceding positions all the way down to the tree’s
root, which contains the target. Branches in the search tree correspond
to complete routes. Calculating the value of branches through task-
dependent scoring functions allows us to compare and rank different
routes. The target molecule can be solved if it can be deconstructed to a
set of readily available building blocks (marked red). Ph, phenyl; Boc,
tert-butyloxycarbonyl; TBS, tert-butyldimethylsilyl.
M.H.S.Segler, et al. Nature 555(2018)
https://www.nature.com/articles/nature25978
49. 48
ARTICLERESEARCH
(1) Selection (2) Expansion (3) Rollout
Pick and evaluate
new position
Incorporate evaluation
in the search tree
Pick most
promising position
Retroanalyse, add new nodes to
tree by expansion procedure (see b)
(4) Update
δQ
δQ
δQ
δ
Invariant
encoding
Expansion policy:
prioritizes
transformations
Keep the k best
transformations and
apply them to
the target
Keep likely
reactions
For each reaction use
in-scope filter
Target
molecule
A
A
Synthesis planning with Monte Carlo tree search
Expansion procedureb
a
A
B
B
C
C
Ranked precursor
molecule positions
T1
T2
.
.
.
Tn
R1
R2
.
.
Rk
ECFP4
Symbolic Neural Neural SymbolicSymbolic
Figure 2 | Schematic of MCTS methodology. a, MCTS searches by
iterating over four phases. In the selection phase (1), the most urgent
node for analysis is chosen on the basis of the current position values.
In phase (2) this node may be expanded by processing the molecules of
the position A with the expansion procedure (b), which leads to new
positions B and C, which are added to the tree. Then, the most promising
new position is chosen, and a rollout phase (3) is performed by randomly
sampling transformations from the rollout policy until all molecules
are solved or a certain depth is exceeded. In the update phase (4), the
position values are updated in the current branch to reflect the result of the
rollout. b, Expansion procedure. First, the molecule (A) to retroanalyse is
converted to a fingerprint and fed into the policy network, which returns a
probability distribution over all possible transformations (T1 to Tn). Then,
only the k most probable transformations are applied to molecule A. This
yields the reactants necessary to make A, and thus complete reactions R1
to Rk. For each reaction, the reaction prediction is performed using the
in-scope filter, returning a probablity score. Improbable reactions are then
filtered out, which leads to the list of admissible actions and corresponding
precursor positions B and C.
M.H.S.Segler, et al. Nature 555(7678), 604 (2018)
https://www.nature.com/articles/nature25978
50. Sequence-to-Sequence
49
del. Seq2seq Model. Neural sequence-to-sequence
eq) models map one sequence to another and have
y shown state of the art performance in many tasks such
hine translation.49,50
It is based on an encoder−decoder
cture that consists of two recurrent neural networks
sequence log probability at each time step during decodi
retained, where N is the width of the beam. The decod
stopped once the lengths of the candidate sequences rea
maximum decode length of 140 characters. The can
sequences that contain an end of sequence charact
considered to be complete. On average, about 97% of all
3. Seq2seq model architecture.
DOI: 10.1021/acscentsc
ACS Cent. Sci. 2017, 3, 11
1105
SMILES
SMILES(SMART)
B.Liu, et al. ACS. Cent. Sci. 3(10), 1103(2017)
https://pubs.acs.org/doi/full/10.1021/acscentsci.7b00303
https://github.com/pandegroup/reaction_prediction_seq2seq
51. ■Coley et al. (2017)
50
tension of the one-step strategy to multistep pathway planning is
.
characters (i.e., a product SMILES26
string without atom
C.W. Coley et al. ACS. Cent. Sci. 3(12), 1237 (2017)
https://pubs.acs.org/doi/full/10.1021/acscentsci.7b00355
https://github.com/connorcoley/retrosim
