Computer-aided Molecular Design Techniques Research Articles

Prospects of Computer-Aided Molecular Design of Coumarins as Ecotoxicologically Safe Plant Protection Agents

Coumarins are secondary plant metabolites widely distributed in higher plants, bacteria, fungi, and sponges. This great structural diversity of these natural compounds and their synthesized derivatives enables their wide range of pharmacological activities, such as antioxidant; antibacterial; antifungal; anti-human immunodeficiency infection; anti-tubercular; and anti-cancer activities, which were very well reviewed previously. There are also many reports about their effectiveness against plant pathogenic pests (phytopathogenic fungi, bacteria, nematodes, and insects). These secondary metabolites protect environmental enemies and competing plants. However, there is still limited literature on coumarins’ practical applications in agriculture, as well as their effects on beneficial populations of soil organisms. This review summarizes recent knowledge about the effects of natural and synthesized coumarins on phytopathogens and beneficial populations of soil organisms. A separate section is dedicated to poorly reviewed computer-aided molecular design (CAMD) methods of agrochemicals. It also reviewed CAMD techniques to develop low-toxicity and environmentally safe pesticides. Despite the many positive effects of coumarins related to plant protection, they do possess properties harmful to the environment and health. These properties are described in the last section. Prediction of coumarin hazardous properties using a quantitative structure-activity relationship (QSAR) approach was also reviewed.

Design of lubricants in electric and hybrid vehicles via computer-aided molecular design (CAMD) technique

The market of electric and hybrid vehicles (EVs and HEVs) is expanding exponentially in recent years. It serves as a greener solution in the transportation sector as compared to internal combustion engine vehicles’ (ICEVs). Conventional lubricants used for ICEVs are not suitable for the EVs and HEVs as the latter operates on electric motors instead of combustion engines. Performance characteristics such as the electrical conductivity, thermal conductivity and copper compatibility are not considered by the ICEVs lubricant but are important for the EVs and HEVs. In this research, computer-aided molecular design technique is used to design the lubricant that is compatible with the EVs and HEVs. The lubricant properties considered are thermal conductivity, specific heat capacity, viscosity, density, and flash point. Group contribution (GC) property prediction model is utilized to ensure that the desired properties are possessed by the generated lubricant. There are six possible solutions of lubricant being generated and discussed. Other properties such as the electrical conductivity, copper corrosivity etc. are reviewed for future development. Additionally, the advantages and challenges of utilizing GC method in designing EVs and HEVs lubricant are discussed. This research serves as guidance for the comprehensive design of advanced lubricant of EVs and HEVs.

In silico design and molecular basis for the selectivity of Olinone toward the first over the second bromodomain of BRD4.

Bromodomains (BrDs), a conserved structural module in chromatin-associated proteins, are well known for recognizing ε-N-acetyl lysine residues on histones. One of the most relevant BrDs is BRD4, a tandem BrD containing protein (BrD1 and BrD2) that plays a critical role in numerous diseases including cancer. Growing evidence shows that the two BrDs of BRD4 have different biological functions; hence selective ligands that can be used to study their functions are of great interest. Here, as a follow-up of our previous work, we first provide a detailed characterization study of the in silico rational design of Olinone as part of a series of five tetrahydropyrido indole-based compounds as BRD4 BrD1 inhibitors. Additionally, we investigated the molecular basis for Olinone's selective recognition by BrD1 over BrD2. Molecular dynamics simulations, free energy calculations, and conformational analyses of the apo-BRD4-BrD1|2 and BRD4-BrD1|2/Olinone complexes showed that Olinone's selectivity is facilitated by five key residues: Leu92 in BrD1|385 in BrD2 of ZA loop, Asn140|433, Asp144|His437 and Asp145|Glu438 of BC loop, and Ile146|Val49 of helix C. Furthermore, the difference in hydrogen bonds number and in mobility of the ZA and BC loops of the acetyl-lysine binding site between BRD4 BrD1/Olinone and BrD2/Olinone complexes also contribute to the difference in Olinone's binding affinity and selectivity toward BrD1 over BrD2. Altogether, our computer-aided molecular design techniques can effectively guide the development of small-molecule BRD4 BrD1 inhibitors, explain their selectivity origin, and further open doors to the design of new therapeutically improved derivatives.

Multi-objective thermo-economic optimization of organic Rankine cycle (ORC) power systems in waste-heat recovery applications using computer-aided molecular design techniques

In this paper, we develop a framework for designing optimal organic Rankine cycle (ORC) power systems that simultaneously considers both thermodynamic and economic objectives. This methodology relies on computer-aided molecular design (CAMD) techniques that allow the identification of an optimal working fluid during the thermo-economic optimization of the system. The SAFT-γ Mie equation of state is used to determine the necessary thermodynamic properties of the designed working fluids, with critical and transport properties estimated using empirical group-contribution methods. The framework is then applied to the design of sub-critical and non-recuperated ORC systems in different applications spanning a range of heat-source temperatures. When minimizing the specific investment cost (SIC) of these systems, it is found that the optimal molecular size of the working fluid is linked to the heat-source temperature, as expected, but also that the introduction of a minimum pinch point constraint that is commonly employed to account for inherent trade-offs between system performance and cost is not necessary. The optimal SICs of waste-heat ORC systems with heat-source temperatures of 150 °C, 250 °C and 350 °C are £10120, £4040 and £2910 per kW, when employing propane, 2-butane and 2-heptene as the working fluids, respectively. During a set of MINLP optimizations of the ORC systems with heat-source temperatures of 150 °C and 250 °C, it is found that 1,3-butadiene and 4-methyl-2-pentene are the best-performing working fluids, respectively, with SICs of £9640 per kW and £4000 per kW. These substances represent novel working fluids for ORC systems that cannot be determined a priori by specifying any working-fluid family or by following traditional methods of testing multiple fluids. Interestingly, the same molecules are identified in a multi-objective optimization considering both the total investment cost and net power output. These findings highlight the power of this approach as it enables the selection of novel working fluids while optimizing ORC systems using single or multiple thermo-economic performance indicators.

Optimal molecular design towards an environmental friendly solvent recovery process

This paper presents a novel Computer Aided Molecular Design (CAMD) methodology for the design of solvents with consideration of environmental impact during the extraction and recovery processes. Throughout the years, CAMD techniques have been widely applied to design novel molecules for various applications, especially solvents. Most of the works in this area are only focusing on designing solvents that can achieve its functionality. However, limited works consider safety, health and environmental impacts of the process in which the solvent is applied. In this work, a quantitative assessment of the total environmental burden for solvent recovery process is incorporated into a single stage CAMD framework. The CAMD formulation includes molecular properties that affect the quantitative assessment of the environmental impact of a process. A multi-objective solvent design framework is then solved using Fuzzy Analytic Hierarchy Process (FAHP) weighting approach to design solvents that satisfy various target properties. With this approach, the generated solvents can simultaneously improve the overall environmental characteristic of a process and give a better balance of performance for a set of predefined properties. To illustrate the proposed methodology, a case study on solvent design for residual oil extraction from palm pressed fibre is presented.

Design of energy efficient reactive solvents for post combustion CO2 capture using computer aided approach

The reduction of CO2 emissions has become a challenge due to rapid economic growth all over the world. Growing concerns on this issue have led the researcher to develop and implement several strategies including one very promising strategy involving CO2 capture from power plants. This paper presents a Computer Aided Molecular Design (CAMD) technique to design new alternative solvents as replacements for commonly used solvents during the post combustion carbon capture process in power plants, which is often costly and energy intensive. The CAMD technique can quickly identify the most suitable solvents and avoid spending resources on infeasible solvent replacements. In this study, the reverse design approach is used where, first, the criteria of the targeted solvents are identified and then the desired solvent candidates generated. The methods for this work consists of five steps: 1) Problem formulation, 2) Generation of single reactive solvent candidate using ProCAMD tools in ICAS software, 3) Prediction of the reaction mechanism between the potential amine solvent candidate and CO2 using the zwitterion mechanism and base-catalysed hydration mechanism, 4) Evaluation of process performance by calculating the heat required for the solvent regeneration process, and 5) Selection of the best solvent candidate based on its desired process performance. The result of this study reveals that 25 amine-based solvents for the chemical absorption process were successfully generated from the amine and alcohol functional groups. By using the proposed solvent, significant savings on regeneration energy, up to 31.4%, could be achieved compared to the conventional solvent, MEA. The percentage average absolute deviation (AAD) of the property prediction model that this study used to estimate the value of heat of formation is 4.67%. This finding is important to further reduce the regeneration energy, which makes the CO2 capture process costly, and achieve a more sustainable and cleaner environment.

Managing Uncertainty on the Integration of Safety and Health Indexes in Computer-Aided Molecular Design

In this work, a novel molecular design methodology has been developed to incorporate the safety and health aspects of potential molecules, while managing the uncertainty in property prediction models applied to measure the safety and health performance. In recent years, various computer-aided molecular design techniques have been developed to design molecules that meet customer requirements. To ensure the selected molecule does not bring harm to the consumers, the concept of inherent safety and health must be incorporated during the synthesis of the molecule. Several safety and health parameters are employed to examine the adverse safety and health impacts that may be posed by the synthesized molecule. These parameters are assessed by the physicochemical properties, which are estimated using property prediction models. Subindex scores are then assigned based on the properties to signify the hazard level. However, there exists uncertainty in the prediction models that can affect the accuracy of the allocated scores. The methodology developed in this work can manage the uncertainty on properties used in the safety and health parameters. The scores at the uncertain region are enhanced to account for uncertainty originated from the prediction models. The quantification of uncertainty ensures that the enhanced scores can better measure the inherent hazard level of the molecule. Hence, the molecular design techniques will be able to identify molecules with less hazards to use in a process without compromising the rest of the target properties.

Small Molecules Targeting the miRNA-Binding Domain of Argonaute 2: From Computer-Aided Molecular Design to RNA Immunoprecipitation.

The development of small-molecule-based target therapy design for human disease and cancer is object of growing attention. Recently, specific microRNA (miRNA) mimicking compounds able to bind the miRNA-binding domain of Argonaute 2 protein (AGO2) to inhibit miRNA loading and its functional activity were described. Computer-aided molecular design techniques and RNA immunoprecipitation represent suitable approaches to identify and experimentally determine if a compound is able to impair the loading of miRNAs on AGO2 protein. Here, we describe these two methodologies that we recently used to select a specific compound able to interfere with the AGO2 functional activity and able to improve the retinoic acid-dependent myeloid differentiation of leukemic cells.

A novel chemical product design framework with the integration of safety and health aspects

Computer-aided molecular design (CAMD) technique is a powerful tool for the design of molecules that meet a set of desirable properties. In most of the CAMD problems, the molecular physical and thermodynamic properties are often selected as the target properties, while safety and health aspects were not taken into consideration. However, certain chemical substances may cause adverse effects to human's health after prolonged and repeated exposure. Therefore, in order to ensure that the generated molecule does not bring harm and health-related risks to the consumers, it is crucial to incorporate both inherent safety and health into the existing CAMD techniques. In this work, a novel chemical product design methodology has been developed to integrate both safety and health aspects into the CAMD framework presented by a single optimisation model. The measurement of safety and health indicators are based on the molecular properties that have impact on both of these aspects. Each property is assigned with an index or penalty value based on the degree of potential hazards. A molecule with a higher index value has a higher hazard level and vice versa. Hence, a molecule that satisfies the target properties and has a low penalty value will be selected as the most reasonable choice. This new approach ensures that a product that possesses the desirable properties, and at the same time meets the safety and health criteria, is produced. A case study on the solvent design for gas sweetening process has been carried out to determine the optimal molecule.

A systematic methodology for optimal mixture design in an integrated biorefinery

Biomass is a sustainable source of energy which can be utilised to produce value-added products such as biochemical products and biomaterials. In order to produce a sustainable supply of such value-added products, an integrated biorefinery is required. An integrated biorefinery is a processing facility that integrates multiple biomass conversion pathways to produce value-added products. To date, various biomass conversion pathways are available to convert biomass into a wide range of products. Due to the large number of available pathways, various systematic screening tools have been developed to address the process design aspect of an integrated biorefinery. Process design however, is often inter-linked with product design as it is important to identify the optimal molecule (based on desired product properties) prior to designing its optimal production routes. In cases where the desired product properties cannot be met by a single component chemical product, a mixture of chemicals would be required. In this respect, product and process design decisions would be a challenging task for an integrated biorefinery. In this work, a novel two-stage optimisation approach is developed to identify the optimal conversion pathways in an integrated biorefinery to convert biomass into the optimal mixtures in terms of target product properties. In the first stage, the optimal mixture is designed via computer-aided molecular design (CAMD) technique. CAMD technique is a reverse engineering approach which predicts the molecules with optimal properties using property prediction models. Different classes of property models such as group contribution (GC) models and quantitative structure property relationship (QSPR) are adapted in this work. The main component of the mixture is first determined from the target product properties. This is followed by the identifying of additive components to form an optimal mixture with the main component based on the desired product properties. Once the optimal mixture is determined, the second stage identifies the optimal conversion pathways via superstructural mathematical optimisation approach. With such approach, the optimal conversion pathways can be determined based on different optimisation objectives (e.g. highest product yield, lowest environmental impact etc.). To illustrate the proposed methodology, a case study on the design of fuel additives as a mixture of different molecules from palm-based biomass is presented. With the developed methodology, optimal fuel additives are designed based on optimal target properties. Once the optimal fuel additives are designed, the optimal conversion pathways in terms of highest product yield and economic performance that convert biomass into the optimal fuel additives are identified.

Robust chemical product design via fuzzy optimisation approach

Traditionally, the design of new chemical products for specific applications is done by using a combination of design heuristics, experimental studies and expert judgements. In addition to the conventional methods, chemical products can also be designed by using computer-aided molecular design (CAMD) techniques. Based on CAMD, optimal chemical products can be designed by identifying the molecule with the best properties that correspond with the target functionalities of the product. In general, the optimality of product property (termed as property superiority) is the only factor considered while designing optimal products by using CAMD techniques. However, it is noted that property prediction models are developed with certain accuracy and uncertainties. As the accuracy of property prediction models (termed as property robustness) can affect the effectiveness of CAMD techniques in predicting the product property, the effects of property prediction uncertainty have to be considered while applying CAMD techniques. This paper presents a systematic fuzzy optimisation based molecular design methodology. The methodology is developed for the design of optimum molecules used in chemical processes by considering and optimising both property superiority and robustness. Property superiority is quantified by property optimality. Meanwhile, property robustness is expressed by the standard deviation of the property prediction model, which is a measure of average variation between the experimental data and estimated values of product property using property prediction model. Fuzzy optimisation approach is extended in this work to address and trade off property superiority and robustness simultaneously. Molecular design technique is adapted in this work to identify the optimal molecular structure which satisfies multiple product specification. To illustrate the proposed method, a case study is presented where optimal solution is selected based on how much the solution satisfied the criteria of property superiority and robustness.

Computer aided polymer design using multi-scale modelling

The ability to predict the key physical and chemical properties of polymeric materials from their repeat-unit structure and chain-length architecture prior to synthesis is of great value for the design of polymer-based chemical products, with new functionalities and improved performance. Computer aided molecular design (CAMD) methods can expedite the design process by establishing input-output relations between the type and number of functional groups in a polymer repeat unit and the desired macroscopic properties. A multi-scale model-based approach that combines a CAMD technique based on group contributionplus models for predicting polymer repeat unit properties with atomistic simulations for providing first-principles arrangements of the repeat units and for predictions of physical properties of the chosen candidate polymer structures, has been developed and tested for design of polymers with desired properties. A case study is used to highlight the main features of this multi-scale model-based approach for the design of a polymer-based product.

Computer-aided polymer design using group contribution plus property models

The preliminary step for polymer product design is to identify the basic repeat unit structure of the polymer that matches the target properties. Computer-aided molecular design (CAMD) approaches can be applied for generating the polymer repeat unit structures that match the required constraints. Polymer repeat unit property prediction models are required to calculate the properties of the generated repeat units. A systematic framework incorporating recently developed group contribution plus (GC +) models and an extended CAMD technique to include design of polymer repeat units is highlighted in this paper. The advantage of a GC + model in CAMD applications is that a very large number of polymer structures can be considered even though some of the group parameters may not be available. A number of case studies involving different polymer design problems have been solved through the developed framework. The paper highlights three such case studies.

Measuring CAMD Technique Performance. 2. How “Druglike” Are Drugs? Implications of Random Test Set Selection Exemplified Using Druglikeness Classification Models

Research into the advancement of computer-aided molecular design (CAMD) has a tendency to focus on the discipline of algorithm development. Such efforts are often wrought to the detriment of the data set selection and analysis used in said algorithm validation. Here we highlight the potential problems this can cause in the context of druglikeness classification. More rigorous efforts are applied to the selection of decoy (nondruglike) molecules from the ACD. Comparisons are made between model performance using the standard technique of random test set creation with test sets derived from explicit ontological separation by drug class. The dangers of viewing druglike space as sufficiently coherent to permit simple classification are highlighted. In addition the issues inherent in applying unfiltered data and random test set selection to (Q)SAR models utilizing large and supposedly heterogeneous databases are discussed.

Patenting computer-designed peptides.

The problem of designing new peptides that possess specific properties, such as bactericidal activity, is of wide interest. Recently, attention has focused on the use of Computer-Aided Molecular Design techniques in parallel with more traditional 'synthesise and test' methods. These techniques may typically use Genetic Algorithms to optimise molecules based on Neural Network models that predict activity. In this paper we describe a successful application of this Molecular Design methodology that has resulted in novel bactericidal peptides of real value. A key issue for commercial utilisation of such results is the ability to protect the intellectual property rights associated with the discovery of new molecules. Typically peptide patents use structural templates of amino acid hydrophobicity-hydrophilicity that define highly regular peptide patent spaces. In an extension of established patenting practice we describe a patent application that uses a Neural Net predictive model to define the regions of peptide space that we claim within the patent. This formalism makes no a priori assumptions about the regularity of the patent space. A preliminary comparative investigation of the shape and size of this and other bactericidal peptide patent spaces is conducted.