Adding Documentation

README.md

@@ -44,3 +44,38 @@ Cheminform._, 2009, 1, 8.
 
 **[[2]](/references/2018/20180108_coley_c_w_et_al.md)** Coley, C.W., Rogers, L., Green, W.H., and Jensen, K.F.
 **SCScore: Synthetic Complexity Learned from a Reaction Corpus**. _J. Chem. Inf. Model._, 2018, 58, 2, 252-261.
+
+**[[3]](/references/2020/20200520_vorsilak_colar_cmelo_and_svozil.md)** Voršilák, M., Kolář, M., Čmelo, I. and Svozil, D. 
+**SYBA: Bayesian estimation of synthetic accessibility of organic compounds**. _J. Cheminform._, 2020, 12, 35.
+
+**[[4]](/references/2021/20210307_thakkar_chadimova_bjerrum_engkvist_reymond.md)** Thakkar, A., Chadimová, V., Bjerrum, E.J., Engkvist, O. and Reymond, J.L.
+**Retrosynthetic accessibility score (RAscore) – rapid machine learned synthesizability classification from AI driven retrosynthetic planning**. _Chem. Sci._, 2021,12, 3339-3349.
+
+**[[5]](/references/2022/20220203_li_and_chen.md)** Baiqing, L. and Hongming, C. **Prediction of Compound Synthesis Accessibility Based on Reaction Knowledge Graph**. _Molecules._, 2022, 27, 1039.
+
+**[[6]](/references/2022/20220422_liu_korablyov_jastrzebski_pruszynski_bengio_segler.md)** Cheng-Hao, L., Korablyov, M., Jastrzębski, S., Włodarczyk-Pruszyński, P., Bengio, Y., and Segler, M.
+**RetroGNN: Fast Estimation of Synthesizability for Virtual Screening and De Novo Design by Learning from Slow Retrosynthesis Software**. _J. Chem. Inf. Model._, 2022, 62, 10, 2293-2300.
+
+**[[7]](/references/2022/20220608_yu_wang_zhao_gao_kang_cao_wang_hou.md)** Yiahui, Y., Wang, J., Zhao, H., Gao, J., Kang, Y., Cao, D., Wang, Z. and Hou, T.
+**Organic Compound Synthetic Accessibility Prediction Based on the Graph Attention Mechanism**. _J. Chem. Inf. Model._, 2022, 62, 12, 2973-2986.
+
+**[[8]](/references/2023/20230831_kim_lee_kim_lim_kim.md)** Hyeongwoo, K., Kyunghoon, L., Chansu, K., Jaechang, L. and Kim, W.Y.
+**DFRscore: Deep Learning-Based Scoring of Synthetic Complexity with Drug-Focused Retrosynthetic Analysis for High-Throughput Virtual Screening**. _J. Chem. Inf. Model._, 2023, 64, 7, 2432-2444.
+
+**[[9]](/references/2023/20230919_parrot_tajmouati-dasilva_atwood_fourcade_mathe_huu_perron.md)** Parrot, M., Tajmouati, H., Ribeiro da Silva, V.B., Atwood, B.R., Fourcade, R., Gaston-Mathé, Y., Do Huu, N., and Perron, Q.
+**Integrating synthetic accessibility with AI-based generative drug design**. _J. Cheminform._, 2023, 15, 83.
+
+**[[10]](/references/2023/20231102_s.wang_l.wang_li_bai.md)** Wang, S., Wang, L., Fenglei, L. and Bai, F.
+**DeepSA: a deep-learning driven predictor of compound synthesis accessibility**. _J. Cheminform._, 2023, 15, 103.
+
+**[[11]](/references/2024/20240723_chen_jung.md)** Chen, S., and Jung, Y. 
+**Estimating the synthetic accessibility of molecules with building block and reaction-aware SAScore**. _J. Cheminform._, 2024, 16, 83.
+
+**[[12]](/references/2024/20241018_neeser_correia_schwaller.md)** Neeser, R.M., Correia, B., Schwaller, P.
+**FSscore: A Personalized Machine Learning-Based Synthetic Feasibility Score**. _Chem. Methods_, 2024, 4, e202400024.
+
+**[[13]](/references/2023/20230114_skoraczynski_kitlas_miasojedow_gambin.md)** Skoraczyński, G., Kitlas, M., Miasojedow, B., and Gambin, A. 
+**Critical assessment of synthetic accessibility scores in computer-assisted synthesis planning**. _J. Cheminform._, 2023, 15, 6.
+
+**[[14]](/references/2024/20240520_raghavan_rago_verma_hassan_goshu_dombrowski_pandey_coley_wang.md)** Raghavan, P., Rago, A.J., Verma, P., Hassan, M.M., Goshu, G.M., Dombrowski, A.W., Pandey, A., Coley, C.W., and Wang, Y. 
+**Incorporating Synthetic Accessibility in Drug Design: Predicting Reaction Yields of Suzuki Cross-Couplings by Leveraging AbbVie’s 15-Year Parallel Library Data Set**. _J. Am. Chem. Soc._, 2024, 146, 22, 15070-15084.

references/2020/20200520_vorsilak_colar_cmelo_and_svozil.md

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+# Overview
+**Title:** SYBA: Bayesian estimation of synthetic accessibility of organic compounds<br>
+**Authors:** SYBA: Milan Voršilák, Michal Kolář, Ivan Čmelo, Daniel Svozil<br>
+**Publication Date:** 2020/05/20<br>
+**Publication Link:** [BMC Journal of Cheminformatics](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-00439-2)
+
+# Abstract
+SYBA (SYnthetic Bayesian Accessibility) is a fragment-based method for the rapid classification of organic 
+compounds as easy- (ES) or hard-to-synthesize (HS). It is based on a Bernoulli naïve Bayes classifier that 
+is used to assign SYBA score contributions to individual fragments based on their frequencies in the database 
+of ES and HS molecules. SYBA was trained on ES molecules available in the ZINC15 database and on HS molecules 
+generated by the Nonpher methodology. SYBA was compared with a random forest, that was utilized as a baseline 
+method, as well as with other two methods for synthetic accessibility assessment: SAScore and SCScore. When used 
+with their suggested thresholds, SYBA improves over random forest classification, albeit marginally, and outperforms 
+SAScore and SCScore. However, upon the optimization of SAScore threshold (that changes from 6.0 to – 4.5), 
+SAScore yields similar results as SYBA. Because SYBA is based merely on fragment contributions, it can be used for 
+the analysis of the contribution of individual molecular parts to compound synthetic accessibility. SYBA is publicly 
+available at https://github.com/lich-uct/syba under the GNU General Public License.

references/2021/20210307_thakkar_chadimova_bjerrum_engkvist_reymond.md

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+# Overview
+**Title:** Retrosynthetic accessibility score (RAscore) – rapid machine learned synthesizability classification from AI driven retrosynthetic planning<br>
+**Authors:** Amol Thakkar, Veronica Chadimová, Esben Jannik Bjerrum, Ola Engkvist, Jean-Louis Reymond<br>
+**Publication Date:** 2021/03/07<br>
+**Publication Link:** [Chemical Science](https://pubs.rsc.org/en/content/articlelanding/2021/sc/d0sc05401a)
+
+# Abstract
+Computer aided synthesis planning (CASP) is part of a suite of artificial intelligence (AI) based 
+tools that are able to propose synthesis routes to a wide range of compounds. However, at present 
+they are too slow to be used to screen the synthetic feasibility of millions of generated or enumerated 
+compounds before identification of potential bioactivity by virtual screening (VS) workflows. Herein 
+we report a machine learning (ML) based method capable of classifying whether a synthetic route can be 
+identified for a particular compound or not by the CASP tool AiZynthFinder. The resulting ML models return 
+a retrosynthetic accessibility score (RAscore) of any molecule of interest, and computes at least 4500 times 
+faster than retrosynthetic analysis performed by the underlying CASP tool. The RAscore should be useful for 
+pre-screening millions of virtual molecules from enumerated databases or generative models for synthetic 
+accessibility and produce higher quality databases for virtual screening of biological activity.

references/2022/20220203_li_and_chen.md

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+# Overview
+**Title:** Prediction of Compound Synthesis Accessibility Based on Reaction Knowledge Graph<br>
+**Authors:** Baiqing Li, Hongming Chen<br>
+**Publication Date:** 2022/02/03<br>
+**Publication Link:** [Molecules](https://www.mdpi.com/1420-3049/27/3/1039)
+
+# Abstract
+With the increasing application of deep-learning-based generative models for de novo molecule design, 
+the quantitative estimation of molecular synthetic accessibility (SA) has become a crucial factor for 
+prioritizing the structures generated from generative models. It is also useful for helping in the 
+prioritization of hit/lead compounds and guiding retrosynthesis analysis. In this study, based on the 
+USPTO and Pistachio reaction datasets, a chemical reaction network was constructed for the identification 
+of the shortest reaction paths (SRP) needed to synthesize compounds, and different SRP cut-offs were then 
+used as the threshold to distinguish a organic compound as either an easy-to-synthesize (ES) or 
+hard-to-synthesize (HS) class. Two synthesis accessibility models (DNN-ECFP model and graph-based CMPNN model) 
+were built using deep learning/machine learning algorithms. Compared to other existing synthesis accessibility 
+scoring schemes, such as SYBA, SCScore, and SAScore, our results show that CMPNN (ROC AUC: 0.791) performs 
+better than SYBA (ROC AUC: 0.76), albeit marginally, and outperforms SAScore and SCScore. Our prediction models 
+based on historical reaction knowledge could be a potential tool for estimating molecule SA.

references/2022/20220422_liu_korablyov_jastrzebski_pruszynski_bengio_segler.md

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+# Overview
+**Title:** RetroGNN: Fast Estimation of Synthesizability for Virtual Screening and De Novo Design by Learning from Slow Retrosynthesis Software<br>
+**Authors:** Cheng-Hao Liu, Maksym Korablyov, Stanisław Jastrzębski, Paweł Włodarczyk-Pruszyński, Yoshua Bengio, Marwin Segler<br>
+**Publication Date:** 2022/04/22<br>
+**Publication Link:** [ACS Publications](https://pubs.acs.org/doi/10.1021/acs.jcim.1c01476)
+
+# Abstract
+De novo molecule design algorithms often result in chemically unfeasible or synthetically inaccessible molecules. 
+A natural idea to mitigate this problem is to bias these algorithms toward more easily synthesizable molecules 
+using a proxy score for synthetic accessibility. However, using currently available proxies can still result in 
+highly unrealistic compounds. Here, we propose a novel approach, RetroGNN, to estimate synthesizability. First, 
+we search for routes using synthesis planning software for a large number of random molecules. This information 
+is then used to train a graph neural network to predict the outcome of the synthesis planner given the target 
+molecule, in which the regression task can be used as a synthesizability scorer. We highlight how RetroGNN can 
+be used in generative molecule-discovery pipelines together with other scoring functions. We evaluate our approach 
+on several QSAR-based molecule design benchmarks, for which we find synthesizable molecules with state-of-the-art 
+scores. Compared to the virtual screening of 5 million existing molecules from the ZINC database, using RetroGNNScore 
+with a simple fragment-based de novo design algorithm finds molecules predicted to be more likely to possess the desired 
+activity exponentially faster, while maintaining good druglike properties and being easier to synthesize. Importantly, 
+our deep neural network can successfully filter out hard to synthesize molecules while achieving a 105 times speedup 
+over using retrosynthesis planning software.

references/2022/20220608_yu_wang_zhao_gao_kang_cao_wang_hou.md

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+# Overview
+**Title:** Organic Compound Synthetic Accessibility Prediction Based on the Graph Attention Mechanism<br>
+**Authors:** Jiahui Yu, Jike Wang, Hong Zhao, Junbo Gao, Yu Kang, Dongsheng Cao, Zhe Wang, Tingjun Hou<br>
+**Publication Date:** 2022/06/08<br>
+**Publication Link:** [ACS Publications](https://pubs.acs.org/doi/10.1021/acs.jcim.2c00038)
+
+# Abstract
+Accurate estimation of the synthetic accessibility of small molecules is needed in many phases of drug discovery. 
+Several expert-crafted scoring methods and descriptor-based quantitative structure–activity relationship (QSAR) 
+models have been developed for synthetic accessibility assessment, but their practical applications in drug discovery 
+are still quite limited because of relatively low prediction accuracy and poor model interpretability. In this study, 
+we proposed a data-driven interpretable prediction framework called GASA (Graph Attention-based assessment of 
+Synthetic Accessibility) to evaluate the synthetic accessibility of small molecules by distinguishing compounds 
+to be easy- (ES) or hard-to-synthesize (HS). GASA is a graph neural network (GNN) architecture that makes self-feature 
+deduction by applying an attention mechanism to automatically capture the most important structural features related to 
+synthetic accessibility. The sampling around the hypothetical classification boundary was used to improve the ability of 
+GASA to distinguish structurally similar molecules. GASA was extensively evaluated and compared with two descriptor-based 
+machine learning methods (random forest, RF; eXtreme gradient boosting, XGBoost) and four existing scores 
+(SYBA: SYnthetic Bayesian Accessibility; SCScore: Synthetic Complexity score; RAscore: Retrosynthetic Accessibility score; 
+SAscore: Synthetic Accessibility score).

references/2023/20230114_skoraczynski_kitlas_miasojedow_gambin.md

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+# Overview
+**Title:** Critical assessment of synthetic accessibility scores in computer-assisted synthesis planning<br>
+**Authors:** Grzegorz Skoraczyński, Mateusz Kitlas, Błażej Miasojedow, Anna Gambin<br>
+**Publication Date:** 2023/01/14<br>
+**Publication Link:** [BMC](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-023-00678-z)
+
+# Abstract
+Modern computer-assisted synthesis planning tools provide strong support for this problem. However, they are still limited 
+by computational complexity. This limitation may be overcome by scoring the synthetic accessibility as a pre-retrosynthesis 
+heuristic. A wide range of machine learning scoring approaches is available, however, their applicability and correctness 
+were studied to a limited extent. Moreover, there is a lack of critical assessment of synthetic accessibility scores with 
+common test conditions.In the present work, we assess if synthetic accessibility scores can reliably predict the outcomes 
+of retrosynthesis planning. Using a specially prepared compounds database, we examine the outcomes of the retrosynthetic 
+tool AiZynthFinder. We test whether synthetic accessibility scores: SAscore, SYBA, SCScore, and RAscore accurately predict 
+the results of retrosynthesis planning. Furthermore, we investigate if synthetic accessibility scores can speed up 
+retrosynthesis planning by better prioritizing explored partial synthetic routes and thus reducing the size of the search 
+space. For that purpose, we analyze the AiZynthFinder partial solutions search trees, their structure, and complexity 
+parameters, such as the number of nodes, or treewidth.We confirm that synthetic accessibility scores in most cases well 
+discriminate feasible molecules from infeasible ones and can be potential boosters of retrosynthesis planning tools. 
+Moreover, we show the current challenges of designing computer-assisted synthesis planning tools.

references/2023/20230831_kim_lee_kim_lim_kim.md

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+# Overview
+**Title:** DFRscore: Deep Learning-Based Scoring of Synthetic Complexity with Drug-Focused Retrosynthetic Analysis for High-Throughput Virtual Screening<br>
+**Authors:** Hyeongwoo Kim, Kyunghoon Lee, Chansu Kim, Jaechang Lim, Woo Youn Kim<br>
+**Publication Date:** 2023/08/31<br>
+**Publication Link:** [ACS Publications](https://pubs.acs.org/doi/10.1021/acs.jcim.3c01134)
+
+# Abstract
+Recently emerging generative AI models enable us to produce a vast number of compounds for potential applications. While 
+they can provide novel molecular structures, the synthetic feasibility of the generated molecules is often questioned. 
+To address this issue, a few recent studies have attempted to use deep learning models to estimate the synthetic accessibility 
+of many molecules rapidly. However, retrosynthetic analysis tools used to train the models rely on reaction templates automatically 
+extracted from a large reaction database that are not domain-specific and may exhibit low chemical correctness. To overcome 
+this limitation, we introduce DFRscore (Drug-Focused Retrosynthetic score), a deep learning-based approach for a more practical 
+assessment of synthetic accessibility in drug discovery. The DFRscore model is trained exclusively on drug-focused reactions, 
+providing a predicted number of minimally required synthetic steps for each compound. This approach enables practitioners to 
+filter out compounds that do not meet their desired level of synthetic accessibility at an early stage of high-throughput virtual 
+screening for accelerated drug discovery. The proposed strategy can be easily adapted to other domains by adjusting the synthesis 
+planning setup of the reaction templates and starting materials.

references/2023/20230919_parrot_tajmouati-dasilva_atwood_fourcade_mathe_huu_perron.md

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+# Overview
+**Title:** Integrating synthetic accessibility with AI-based generative drug design<br>
+**Authors:** Maud Parrot, Hamza Tajmouati, Vinicius Barros Ribeiro da Silva, Brian Ross Atwood, Robin Fourcade, Yann Gaston-Mathé, Nicolas Do Huu, Quentin Perron<br>
+**Publication Date:** 2023/09/19<br>
+**Publication Link:** [BMC](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-023-00742-8)
+
+# Abstract
+Generative models are frequently used for de novo design in drug discovery projects to propose new molecules. However, 
+the question of whether or not the generated molecules can be synthesized is not systematically taken into account 
+during generation, even though being able to synthesize the generated molecules is a fundamental requirement for such 
+methods to be useful in practice. Methods have been developed to estimate molecule “synthesizability”, but, so far, 
+there is no consensus on whether or not a molecule is synthesizable. In this paper we introduce the Retro-Score (RScore), 
+which computes a synthetic accessibility score of molecules by performing a full retrosynthetic analysis through our 
+data-driven synthetic planning software Spaya, and its dedicated API: Spaya-API (https://spaya.ai). We start by comparing 
+several synthetic accessibility scores to a binary “chemist score” as estimated by chemists on a bench of generated molecules, 
+as a first experimental validation that the RScore is a reliable synthetic accessibility score. We then describe a pipeline 
+to generate molecules that validate a list of targets while still being easy to synthesize. We further this idea by performing 
+experiments comparing molecular generator outputs across a range of constraints and conditions. We show that the RScore can 
+be learned by a Neural Network, which leads to a new score: RSPred. We demonstrate that using the RScore or RSPred as a 
+constraint during molecular generation enables our molecular generators to produce more synthesizable solutions, 
+with higher diversity. 

references/2023/20231102_s.wang_l.wang_li_bai.md

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+# Overview
+**Title:** DeepSA: a deep-learning driven predictor of compound synthesis accessibility<br>
+**Authors:** Shihang Wang, Lin Wang, Fenglei Li, Fang Bai<br>
+**Publication Date:** 2023/11/02<br>
+**Publication Link:** [BMC](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-023-00771-3)
+
+# Abstract
+With the continuous development of artificial intelligence technology, more and more computational models for generating 
+new molecules are being developed. However, we are often confronted with the question of whether these compounds are easy 
+or difficult to synthesize, which refers to synthetic accessibility of compounds. In this study, a deep learning based 
+computational model called DeepSA, was proposed to predict the synthesis accessibility of compounds, which provides a useful 
+tool to choose molecules. DeepSA is a chemical language model that was developed by training on a dataset of 3,593,053 molecules 
+using various natural language processing (NLP) algorithms, offering advantages over state-of-the-art methods and having a much 
+higher area under the receiver operating characteristic curve (AUROC), i.e., 89.6%, in discriminating those molecules that are 
+difficult to synthesize. This helps users select less expensive molecules for synthesis, reducing the time and cost required 
+for drug discovery and development. Interestingly, a comparison of DeepSA with a Graph Attention-based method shows that 
+using SMILES alone can also efficiently visualize and extract compound’s informative features.