支持向量机与KStar模型预测细胞色素P450酶催化的氧脱烃反应

分别以支持向量机(SVM)和KStar方法为基础, 构建了代谢产物的分子形状判别和代谢反应位点判别的嵌套预测模型. 分子形状判别模型是以272个分子为研究对象, 计算了包括分子拓扑、二维自相关、几何结构等在内的1280个分子描述符, 考查了支持向量机、决策树、贝叶斯网络、k最近邻这四种机器学习方法建立分类预测模型的准确性. 结果表明, 支持向量机优于其他方法, 此模型可用于预测分子能否被细胞色素P450酶催化发生氧脱烃反应. 代谢反应位点判别模型以538个氧脱烃反应代谢位点为研究对象, 计算了表征原子能量、价态、电荷等26个量子化学特征, 比较了决策树、贝叶斯网络、KStar、人工神经网络建模的准确率. 结果显示, KStar模型的准确率、敏感性、专一性均在90%以上, 对分子形状判别模型筛选出的分子, 此模型能较好地判断出哪个C―O键发生断裂. 本文以15个代谢反应明确的中药分子为验证集, 验证模型准确性, 研究结果表明基于SVM和KStar的嵌套预测模型具有一定的准确性, 有助于开展中药分子氧脱烃代谢产物的预测研究.     

基于思维模型建构的高三有机化学复习* ——以“基础有机合成路线设计的反应先后次序”为例

正1整体教学构思学生在真实情境中合成具有挑战性的目标分子时常出现错误,究其原因是对"结构决定性质"的学科思想缺乏深刻理解。依据物质结构知识探析有机反应规律,建立相关反应模型;结合情境设置挑战性问题,运用反应规律与模型,基于"切断法"的逆合成分析发展逆向思维能力,帮助学生将有机合成知识结构化,反思建构有机合成路线设计的反应次序思维模型。     

“一锅法”不对称多组分串联反应合成手性氢化环氧异色烯衍生物:一种快速构建分子复杂性方法（英文）

设计、研究了一种重氮、4-羟基丙烯酸乙酯和呋喃衍生物参与的三组分串联反应,在Rh2(OAc)4/Zr-3-I-联二萘酚共催化下,该反应能快速高效地一锅构建复杂O-杂并环化合物.利用该方法,可从简单起始原料高立体选择性地合成含有八个连续手性中心的氢化环氧异色烯衍生物,非对映选择性和高对映选择性分别高达99/1 dr和99%ee.结合理论计算对反应的高立体选择性进行了探讨.可能的反应机理为羟基叶立德捕捉的多组分反应后发生分子内的Diels-Alder反应,再发生双键环氧化反应.该方法为复杂O-并杂环化合物的高效合成提供了一种新途径,同时也为高效构建分子复杂性提供了一种新思路,在基于表型筛选的新药发现中具有重要潜在应用.     

(7R,10S)-Boivinianin B的不对称合成

报道了一种基于Dehydro-α-curcumene(4)为关键中间体, 以Sharpless不对称双羟化反应和碘催化的分子内环化成醚反应为关键步骤, 构建2,2,5位三取代的四氢呋喃骨架的方法, 完成了(7R,10S)-Boivinianin B(1)的不对称合成. 合成路线简捷, 对此类化合物的合成有一定的借鉴意义.     

化学反应处理的计算模型

介绍了一种将同类反应上升为合成反应知识和在计算机上实现反合成分析的方法,反合成分析是合成设计中最关键的一步,在本工作中采用了基于谋略键寻找的合成设计方法。它有逻辑宜于在计算机上实现的优点。为了实现这个方法,我们首次提出了一种能中肯地描述合成反应的计算模型—反应知识的分类模型。这一模型由三条规则定义：规则A-反应类型;规则B-发生反应的外部条件;规则C-不适宜采用这个反应的情况;这种计算模型能够将海量反应数据中最重要最基本的信息提炼出来,转换成计算机能处理的知识。它也包含有反应适用范围的信息,从而提高了析分过程的外推能力。     

机器学习设计新型有机分子研究进展

新型有机分子一直是有机化学领域的研究重点,其在开发高性能材料方面具有重要意义.传统的有机分子发现是一个类似于"炒菜"的试错过程,它耗时耗能且效率相对低下.常见的量子化学方法试图根据期望属性值筛选出合理的分子结构,以更好地指导实验,然而,由于计算资源相对于算法复杂度严重不足,精确给出实验指导在大多数情况下难以实现.近年来机器学习的出现改变了这种情况,训练好的模型可以快速推测出分子的属性.更令人兴奋的是机器学习可以逆向进行分子设计,拓宽人类的想象力,给出其在分子设计领域的"神之一手".本综述首先介绍了逆向分子设计所必须的分子描述方式,随后对几种常见的深度生成模型加以归纳,对新型有机分子设计研究现状进行了总结,最后探讨了新型有机分子设计所面临的挑战,展示了笔者做出的部分探索.     

过氧化氢检测用荧光探针的设计、合成及性能

基于逆向合成分析的方法,以水杨醛吖嗪作为传感产物,设计合成了一种用于检测H_2O_2的"开启式"荧光探针。在室温条件下向2-甲酰基苯基硼酸频哪醇酯中加入过量水合肼,通过一步反应和低成本合成,得到一种含硼氮杂环化合物。经X-射线单晶衍射确认此化合物和H_2O_2的反应产物为水杨醛吖嗪,因此达到检测溶液/薄膜中H_2O_2的目的。在溶液含水量为90%,酸碱度为中性的条件下,H_2O_2浓度从0增加到100μmol/L,通过光谱仪测得探针分子荧光增强约120倍,同时在含有其它氧化物的溶液中,荧光响应微弱。该探针具有优异的选择性和高灵敏度(2. 1×10~(-7)mol/L)。     

杂环烯酮缩胺:构筑分子多样性稠杂环化合物的合成先导分子

杂环烯酮缩胺（HKAs）是一类构建分子多样性稠杂环化合物的多功能合成砌块,已被广泛应用于构筑多种多样的类天然杂环化合物和合成药物中。随着杂环烯酮缩胺研究的深入开展,其作为双亲核试剂与多种亲电底物反应取得了较大的进展。本文将基于HKAs的结构特征、反应性能,对以HKAs及1,1-烯二胺为合成子构建各种类型具有潜在生物活性的类天然多环稠杂环化合物进行综述。     

有机催化在不对称构建含氮杂环化合物中的应用

含氮杂环结构广泛存在于天然产物及药物分子中,具有广谱的生物和药理活性,受到有机化学家及药物化学家们的高度关注.开发其绿色、高效的合成方法,尤其是不对称合成方法一直是热门研究课题.近年来,基于脯氨酸等氨基酸手性模块设计合成的系列有机小分子催化剂不断被报道,为不对称构建含氮杂环化合物提供了高效快捷的途径.综述了我们研究小组在有机催化不对称构建含氮杂环化合物领域的研究成果,讨论了关键反应涉及的催化机理.     

末端含三甲基硅基树枝状低聚苯的合成与表征

树枝状聚合物是一类结构有序、有特定分子量、末端可带活性官能团的多功能聚合物,其应用研究涉及信息贮存材料、高级催化剂、非线性光学材料、液晶材料、纳米材料、缓释药物载体、传感器材料、污水处理剂、分离膜及流变学改性剂等领域.以含多功能团的低聚苯为中心核,通过过渡金属催化的芳基偶联反应或Diels-Alder环加成反应,经“收敛法”或“发散法”可以制得结构准确、尺寸可控的树枝状聚苯纳米材料;另一方面,由于核心分子结构的多样性,可以设计、合成拓扑形态各异的树枝状聚苯应用于有机发光材料、有机磁性体、碟状液晶、管束状分子通道、分子识别、储氢材料及锂电池等领域,从而丰富其结构与性能关系的研究内容.因此,树枝状聚苯中心核的设计与合成在这类材料的应用研究中显得尤为重要.本工作设计与合成了一类树枝状聚苯的中心核12和13,其分子末端的生长点被三甲基硅基（TMS-）所保护;采用凝胶渗透色谱（GPC）和粉末X射线衍射等分析手段,以及与其母体结构,即末端不含三甲基硅基的模型化合物1,3,5-三（3＇,5＇-二苯基苯基）苯11进行比较,探讨了分子末端的三甲基硅基及其取代位置对树枝状低聚苯的凝胶渗透色谱行为和结晶性的影响.     

Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy

We present an extension of our Molecular Transformer model combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention. The single-step retrosynthetic model sets a new state of the art for predicting reactants as well as reagents, solvents and catalysts for each retrosynthetic step. We introduce four metrics (coverage, class diversity, round-trip accuracy and Jensen–Shannon divergence) to evaluate the single-step retrosynthetic models, using the forward prediction and a reaction classification model always based on the transformer architecture. The hypergraph is constructed on the fly, and the nodes are filtered and further expanded based on a Bayesian-like probability. We critically assessed the end-to-end framework with several retrosynthesis examples from literature and academic exams. Overall, the frameworks have an excellent performance with few weaknesses related to the training data. The use of the introduced metrics opens up the possibility to optimize entire retrosynthetic frameworks by focusing on the performance of the single-step model only.

We present an extension of our Molecular Transformer model combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention.     

Evaluating and clustering retrosynthesis pathways with learned strategy

With recent advances in the computer-aided synthesis planning (CASP) powered by data science and machine learning, modern CASP programs can rapidly identify thousands of potential pathways for a given target molecule. However, the lack of a holistic pathway evaluation mechanism makes it challenging to systematically prioritize strategic pathways except for using some simple heuristics. Herein, we introduce a data-driven approach to evaluate the relative strategic levels of retrosynthesis pathways using a dynamic tree-structured long short-term memory (tree-LSTM) model. We first curated a retrosynthesis pathway database, containing 238k patent-extracted pathways along with ∼55 M artificial pathways generated from an open-source CASP program, ASKCOS. The tree-LSTM model was trained to differentiate patent-extracted and artificial pathways with the same target molecule in order to learn the strategic relationship among single-step reactions within the patent-extracted pathways. The model achieved a top-1 ranking accuracy of 79.1% to recognize patent-extracted pathways. In addition, the trained tree-LSTM model learned to encode pathway-level information into a representative latent vector, which can facilitate clustering similar pathways to help illustrate strategically diverse pathways generated from CASP programs.

Tree-structured long short-term memory neural model learns to understand the retrosynthesis design strategies from patent-extracted retrosynthetic pathway data.     

Automatic retrosynthetic route planning using template-free models

Retrosynthetic route planning can be considered a rule-based reasoning procedure. The possibilities for each transformation are generated based on collected reaction rules, and then potential reaction routes are recommended by various optimization algorithms. Although there has been much progress in computer-assisted retrosynthetic route planning and reaction prediction, fully data-driven automatic retrosynthetic route planning remains challenging. Here we present a template-free approach that is independent of reaction templates, rules, or atom mapping, to implement automatic retrosynthetic route planning. We treated each reaction prediction task as a data-driven sequence-to-sequence problem using the multi-head attention-based Transformer architecture, which has demonstrated power in machine translation tasks. Using reactions from the United States patent literature, our end-to-end models naturally incorporate the global chemical environments of molecules and achieve remarkable performance in top-1 predictive accuracy (63.0%, with the reaction class provided) and top-1 molecular validity (99.6%) in one-step retrosynthetic tasks. Inspired by the success rate of the one-step reaction prediction, we further carried out iterative, multi-step retrosynthetic route planning for four case products, which was successful. We then constructed an automatic data-driven end-to-end retrosynthetic route planning system (AutoSynRoute) using Monte Carlo tree search with a heuristic scoring function. AutoSynRoute successfully reproduced published synthesis routes for the four case products. The end-to-end model for reaction task prediction can be easily extended to larger or customer-requested reaction databases. Our study presents an important step in realizing automatic retrosynthetic route planning.

Retrosynthetic pathway planning using a template-free model coupled with heuristic Monte Carlo tree search.     

Retrosynthetic accessibility score (RAscore) – rapid machine learned synthesizability classification from AI driven retrosynthetic planning

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.

The retrosynthetic accessibility score (RAscore) is based on AI driven retrosynthetic planning, and is useful for rapid scoring of synthetic feasability and pre-screening of large datasets of virtual/generated molecules.     

Letter: on the retrosynthetic process of some 2-pyridinecarboxamides in electrospray ionisation conditions

The occurrence of retrosynthetic processes, usually observed in electron ionization conditions, can be considered of interest, giving information of on possible new synthetic routes. Generally, retrosynthesis is typical of odd electron ion species, but in the present case it has been observed for electrospray (ESI) generated [MH]+ species of 2-pyridinecarboxamides. In fact, either in the ESI spectrum or in the MS/MS spectrum of [MH]+ species, a primary water loss is observed, giving rise to the molecular ion of the corresponding imidazolines, employed as synthons for the title compounds. The breakdown curves related to this water loss has been determined, indicating that this process is more favoured for the bromine-containing molecules.     

BAND NN: A Deep Learning Framework for Energy Prediction and Geometry Optimization of Organic Small Molecules

Recent advances in artificial intelligence along with the development of large data sets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far require the atomic positions obtained from geometry optimizations using high-level QM/DFT methods as input in order to predict the energies and do not allow for geometry optimization. In this study, a transferable and molecule size-independent machine learning model bonds (B), angles (A), nonbonded (N) interactions, and dihedrals (D) neural network (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and nonequilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N), and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational, and reaction space. The transferability of this model on systems larger than the ones in the data set is demonstrated by performing calculations on selected large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from nonequilibrium structures along with predicting their energies. © 2019 Wiley Periodicals, Inc.     

量子机器学习在催化化学研究中的应用

量子机器学习融合了量子化学与机器学习的优点,具有比传统密度泛函理论更快的计算速度和更高的准确性.量子机器学习可为复杂、多维、多尺度的催化化学提供更智能和有效的研究方式,通过训练可靠的数据及建立合理的模型和算法,快速、准确地预测最优的催化剂设计参数、最佳的催化剂材料的合成方法和反应条件、以及催化剂结构和性能之间的关系.作者就量子机器学习应用于催化材料的设计、催化反应性能和催化反应机理三方面的发展趋势进行了概述.     

Synergy Between Expert and Machine‐Learning Approaches Allows for Improved Retrosynthetic Planning

When computers plan multistep syntheses, they can rely either on expert knowledge or information machine‐extracted from large reaction repositories. Both approaches suffer from imperfect functions evaluating reaction choices: expert functions are heuristics based on chemical intuition, whereas machine learning (ML) relies on neural networks (NNs) that can make meaningful predictions only about popular reaction types. This paper shows that expert and ML approaches can be synergistic—specifically, when NNs are trained on literature data matched onto high‐quality, expert‐coded reaction rules, they achieve higher synthetic accuracy than either of the methods alone and, importantly, can also handle rare/specialized reaction types.     

基于支持向量学习机的HIV-1蛋白酶抑制剂的活性预测

为了预测人体免疫缺陷蛋白酶抑制剂的活性, 计算了表征分子的组成和拓扑特征的462个分子描述符, 用Kennard-Stone方法和随机方法进行了训练集和测试集设计, 用Monte Carlo 模拟退火方法进行变量筛选, 并分别用神经网络, 逻辑回归, k-近邻和支持向量学习机方法建立了HIV-1蛋白酶的抑制剂模型. 结果表明支持向量学习机优于其余机器学习方法, 用SVM方法所建立的最优模型的最后预测正确率达到98.24%.     

Machine learning methods for property prediction in chemoinformatics: Quo Vadis?

This paper is focused on modern approaches to machine learning, most of which are as yet used infrequently or not at all in chemoinformatics. Machine learning methods are characterized in terms of the "modes of statistical inference" and "modeling levels" nomenclature and by considering different facets of the modeling with respect to input/ouput matching, data types, models duality, and models inference. Particular attention is paid to new approaches and concepts that may provide efficient solutions of common problems in chemoinformatics: improvement of predictive performance of structure-property (activity) models, generation of structures possessing desirable properties, model applicability domain, modeling of properties with functional endpoints (e.g., phase diagrams and dose-response curves), and accounting for multiple molecular species (e.g., conformers or tautomers).