Review of Computational approaches for predicting the physicochemical and biological properties of nanoparticles

In the growing field of nanotechnology there is a need to determine the physicochemical and potential toxicological properties of nanomaterials since many industrial, medical and consumer applications are based on an understanding of these properties and on a controlled exposure to the materials. This document provides a literature review on the current status of computational studies aimed at predicting the physicochemical properties and biological effects (including toxicity) of nanomaterials, with an emphasis on medical applications. Although a number of models have been published for physicochemical property prediction, very few models have been published for predicting biological effects, toxicity or the underlying mechanisms of action. This is due to two main reasons: a) nanomaterials form a colloidal phase when in contact with biological systems making the definition and calculation of properties (descriptors) suitable for the prediction of toxicity a new and challenging task, and b) nanomaterials form a very heterogeneous class of materials, not only in terms of their chemical composition, but also in terms of size, shape, agglomeration state, and surface reactivity. There is thus an urgent need to extend the traditional structure-activity paradigm to develop methods for predicting the toxicity of nanomaterials, and to make the resulting models readily available. This document concludes by proposing some lines of research to fill the gap in knowledge and predictive methodologyJRC.I.6-Systems toxicolog

A General Treatment of Solubility. 3. Principal Component Analysis (PCA) of the Solubilities of Diverse Solutes in Diverse Solvents

Article discussing a general treatment of solubility and principle component analysis (PCA) of the solubilities of diverse solutes in diverse solvents

A General Treatment of Solubility 4. Description and Analysis of a PCA Model for Ostwald Solubility Coefficients

Article discussing a general treatment of solubility and a description and analysis of a principal component analysis (PCA) model for Ostwald solubility coefficients

Solvation thermodynamics of organic molecules by the molecular integral equation theory : approaching chemical accuracy

The integral equation theory (IET) of molecular liquids has been an active area of academic research in theoretical and computational physical chemistry for over 40 years because it provides a consistent theoretical framework to describe the structural and thermodynamic properties of liquid-phase solutions. The theory can describe pure and mixed solvent systems (including anisotropic and nonequilibrium systems) and has already been used for theoretical studies of a vast range of problems in chemical physics / physical chemistry, molecular biology, colloids, soft matter, and electrochemistry. A consider- able advantage of IET is that it can be used to study speci fi c solute − solvent interactions, unlike continuum solvent models, but yet it requires considerably less computational expense than explicit solvent simulations

Soluut-solvent vastasmõjude eksperimentaalne uurimine ja modelleerimine

Väitekirja elektrooniline versioon ei sisalda publikatsiooneEnamik praktilist tähtsust omavatest keemilistest protsessidest toimub vedelikes – mitte ainult tööstuslik süntees ja laborikeemia, vaid ka bioloogilised protsessid nagu rakkude hingamine toimuvad molekulaarsel tasemel keerulise koostisega lahustes. Neist protsessidest arusaamine ja molekulide käitumise ennustamine lahustes on tähtis arvukate uurimisvaldkondade jaoks, meditsiinist ja farmakoloogiast naftakeemiani. Kahjuks on ainete omaduste ennustamine vedelikes arvutuskeemia jaoks üks keerulisemaid ülesandeid. Käesolevas töös hinnati olemasolevate arvutusmetoodikate sobivust vesiniksideme tekke kirjeldamiseks orgaanilistes lahustites ning molekulide jaotuse kirjeldamiseks kahe vedeliku vahel (sisuliselt vedelik-vedelik ekstraktsiooni modelleerimiseks). Peamine kasutatud arvutusmeetod oli COSMO-RS (Conductor-like Screening Model for Real Solvents), valitud oma erakordse sobivuse tõttu kontsentreeritud ja mitmekomponendiliste lahuste omaduste ennustamiseks ja molekulaardisainiks. Töö käigus leiti, et vesiniksidemed neutraalsete molekulide vahel on kirjeldatavad suhteliselt hästi, kuid vaadeldud arvutusmetoodikad pole piisavalt täpsed negatiivselt laetud vesiniksidemega komplekside modelleerimiseks. Vedelik-vedelik ekstraktsiooni tulemuste ennustamine COSMO-RS meetodiga oli üldjuhul edukas. Saadud tulemustele (nii lõpp- kui vahepealsetele parameetritele) saadi täpsuse hinanngud. Peale selle arendati uus metodoloogia tundmatute ühendite jaotuse ennustamiseks kahe mitteseguneva vedeliku vahel ilma vajaduseta ühendeid identifitseerida. See lihtsustab parima lahusti valikut ainete isoleerimiseks või puhastamiseks, vähendades töö- ja kemikaalide kulu ning jäätmete kogust.The majority of practically relevant chemical processes occur in liquids. Those are not limited to industrial synthesis and laboratory chemistry – biological processes such as cellular respiration on molecular level take place in complex solutions. Understanding and being able to predict the behaviour of molecules in solutions is essential for numerous branches of science, ranging from medicine and pharmacology to petroleum chemistry. However, predicting the behavior of chemical compounds in liquids, especially in many-component solutions, is one of the most challenging tasks for computational chemistry. In this work existing computational methodologies were tested for suitability for describing hydrogen bond formation in organic solvents and distribution of organic compounds between liquids (essentially modeling of liquid-liquid extraction). The main computational method in this work is COSMO-RS (Conductor-like Screening Model for Real Solvents), chosen for its unequalled ability to predict properties of concentrated and multicomponent solutions and usability in molecular design. It was found that properties of hydrogen bonds between uncharged molecules can be predicted relatively well, but the tested computational approaches were not accurate enough for description of hydrogen bonds involving negatively charged ions. Modeling of liquid-liquid extraction using COSMO-RS was generally successful. Accuracy of the predictions and intermediate parameters was evaluated and problematic cases identified and discussed. Also, a new methodology was developed for predicting the distribution of unknown compounds between immiscible solutions without need for compound identification. It allows simplifying the solvent selection for compound isolation or purification, reducing the workload, expenses and waste amount

심층학습을 이용한 액체계의 성질 예측

학위논문(박사)--서울대학교 대학원 :자연과학대학 화학부,2020. 2. 정연준.최근 기계학습 기술의 급격한 발전과 이의 화학 분야에 대한 적용은 다양한 화학적 성질에 대한 구조-성질 정량 관계를 기반으로 한 예측 모형의 개발을 가속하고 있다. 용매화 자유 에너지는 그러한 기계학습의 적용 예중 하나이며 다양한 용매 내의 화학반응에서 중요한 역할을 하는 근본적 성질 중 하나이다. 본 연구에서 우리는 목표로 하는 용매화 자유 에너지를 원자간의 상호작용으로부터 구할 수 있는 새로운 심층학습 기반 용매화 모형을 소개한다. 제안된 심층학습 모형의 계산 과정은 용매와 용질 분자에 대한 부호화 함수가 각 원자와 분자들의 구조적 성질에 대한 벡터 표현을 추출하며, 이를 토대로 원자간 상호작용을 복잡한 퍼셉트론 신경망 대신 벡터간의 간단한 내적으로 구할 수 있다. 952가지의 유기용질과 147가지의 유기용매를 포함하는 6,493가지의 실험치를 토대로 기계학습 모형의 교차 검증 시험을 실시한 결과, 평균 절대 오차 기준 0.2 kcal/mol 수준으로 매우 높은 정확도를 가진다. 스캐폴드-기반 교차 검증의 결과 역시 0.6 kcal/mol 수준으로, 외삽으로 분류할 수 있는 비교적 새로운 분자 구조에 대한 예측에 대해서도 우수한 정확도를 보인다. 또한, 제안된 특정 기계학습 모형은 그 구조 상 특정 용매에 특화되지 않았기 때문에 높은 양도성을 가지며 학습에 이용할 데이터의 수를 늘이는 데 용이하다. 원자간 상호작용에 대한 분석을 통해 제안된 심층학습 모형 용매화 자유 에너지에 대한 그룹-기여도를 잘 재현할 수 있음을 알 수 있으며, 기계학습을 통해 단순히 목표로 하는 성질만을 예측하는 것을 넘어 더욱 상세한 물리화학적 이해를 하는 것이 가능할 것이라 기대할 수 있다.Recent advances in machine learning technologies and their chemical applications lead to the developments of diverse structure-property relationship based prediction models for various chemical properties; the free energy of solvation is one of them and plays a dominant role as a fundamental measure of solvation chemistry. Here, we introduce a novel machine learning-based solvation model, which calculates the target solvation free energy from pairwise atomistic interactions. The novelty of our proposed solvation model involves rather simple architecture: two encoding function extracts vector representations of the atomic and the molecular features from the given chemical structure, while the inner product between two atomistic features calculates their interactions, instead of black-boxed perceptron networks. The cross-validation result on 6,493 experimental measurements for 952 organic solutes and 147 organic solvents achieves an outstanding performance, which is 0.2 kcal/mol in MUE. The scaffold-based split method exhibits 0.6 kcal/mol, which shows that the proposed model guarantees reasonable accuracy even for extrapolated cases. Moreover, the proposed model shows an excellent transferability for enlarging training data due to its solvent-non-specific nature. Analysis of the atomistic interaction map shows there is a great potential that our proposed model reproduces group contributions on the solvation energy, which makes us believe that the proposed model not only provides the predicted target property, but also gives us more detailed physicochemical insights.1. Introduction 1 2. Delfos: Deep Learning Model for Prediction of Solvation Free Energies in Generic Organic Solvents 7 2.1. Methods 7 2.1.1. Embedding of Chemical Contexts 7 2.1.2. Encoder-Predictor Network 9 2.2. Results and Discussions 13 2.2.1. Computational Setup and Results 13 2.2.2. Transferability of the Model for New Compounds 17 2.2.3. Visualization of Attention Mechanism 26 3. Group Contribution Method for the Solvation Energy Estimation with Vector Representations of Atom 29 3.1. Model Description 29 3.1.1. Word Embedding 29 3.1.2. Network Architecture 33 3.2. Results and Discussions 39 3.2.1. Computational Details 39 3.2.2. Prediction Accuracy 42 3.2.3. Model Transferability 44 3.2.4. Group Contributions of Solvation Energy 49 4. Empirical Structure-Property Relationship Model for Liquid Transport Properties 55 5. Concluding Remarks 61 A. Analyzing Kinetic Trapping as a First-Order Dynamical Phase Transition in the Ensemble of Stochastic Trajectories 65 A1. Introduction 65 A2. Theory 68 A3. Lattice Gas Model 70 A4. Mathematical Model 73 A5. Dynamical Phase Transitions 75 A6. Conclusion 82 B. Reaction-Path Thermodynamics of the Michaelis-Menten Kinetics 85 B1. Introduction 85 B2. Reaction Path Thermodynamics 88 B3. Fixed Observation Time 94 B4. Conclusions 101Docto

Multi-solvent models for solvation free energy predictions using 3D-RISM hydration thermodynamic descriptors

The potential to predict Solvation Free Energies (SFEs) in any solvent using a machine learning (ML) model based on thermodynamic output, extracted exclusively from 3D-RISM simulations in water is investigated. The models on multiple solvents take into account both the solute and solvent description and offer the possibility to predict SFEs of any solute in any solvent with root mean squared errors less than 1 kcal/mol. Validations that involve exclusion of fractions or clusters of the solutes or solvents exemplify the model’s capability to predict SFEs of novel solutes and solvents with diverse chemical profiles. In addition to being predictive, our models can identify the solute and solvent features that influence SFE predictions. Furthermore, using 3D-RISM hydration thermodynamic output to predict SFEs in any organic solvent reduces the need to run 3D-RISM simulations in all these solvents. Altogether, our multi-solvent models for SFE predictions that take advantage of the solvation effects are expected to have an impact in the property prediction space

Quantitative Structure–Property Relationship Studies on Ostwald Solubility and Partition Coefficients of Organic Solutes in Ionic Liquids

Article discussing quantitative structure-property relationship studies on Ostwald solubility and partition coefficients of organic solutes in ionic liquids