Applications of Quantum Chemistry in Pharmaceutical Process Development: Current State and Opportunities

A hybrid quantum computing pipeline for real world drug discovery

Quantum computing, with its superior computational capabilities compared to classical approaches, holds the potential to revolutionize numerous scientific domains, including pharmaceuticals. However, the application of quantum computing for drug discovery has primarily been limited to proof-of-concept studies, which often fail to capture the intricacies of real-world drug development challenges. In this study, we diverge from conventional investigations by developing a hybrid quantum computing pipeline tailored to address genuine drug design problems. Our approach underscores the application of quantum computation in drug discovery and propels it towards more scalable system. We specifically construct our versatile quantum computing pipeline to address two critical tasks in drug discovery: the precise determination of Gibbs free energy profiles for prodrug activation involving covalent bond cleavage, and the accurate simulation of covalent bond interactions. This work serves as a pioneering effort in benchmarking quantum computing against veritable scenarios encountered in drug design, especially the covalent bonding issue present in both of the case studies, thereby transitioning from theoretical models to tangible applications. Our results demonstrate the potential of a quantum computing pipeline for integration into real world drug design workflows.

Linking solid-state phenomena via energy differences in `archetype crystal structures'

Categorization underlies understanding. Conceptualizing solid-state structures of organic molecules with `archetype crystal structures' bridges established categories of disorder, polymorphism and solid solutions and is herein extended to special position and high-Z' structures. The concept was developed in the context of disorder modelling [Dittrich, B. (2021). IUCrJ, 8, 305-318] and relies on adding quantum chemical energy differences between disorder components to other criteria as an explanation as to why disorder - and disappearing disorder - occurs in an average structure. Part of the concept is that disorder, as probed by diffraction, affects entire molecules, rather than just the parts of a molecule with differing conformations, and the finding that an R·T energy difference between disorder archetypes is usually not exceeded. An illustrative example combining disorder and special positions is the crystal structure of oestradiol hemihydrate analysed here, where its space-group/subgroup relationship is required to explain its disorder of hydrogen-bonded hydrogen atoms. In addition, we show how high-Z' structures can also be analysed energetically and understood via archetypes: high-Z' structures occur when an energy gain from combining different rather than overall alike conformations in a crystal significantly exceeds R·T, and this finding is discussed in the context of earlier explanations in the literature. Twinning is not related to archetype structures since it involves macroscopic domains of the same crystal structure. Archetype crystal structures are distinguished from crystal structure prediction trial structures in that an experimental reference structure is required for them. Categorization into archetype structures also has practical relevance, leading to a new practice of disorder modelling in experimental least-squares refinement alluded to in the above-mentioned publication.

Study on the Effect of Cations on the Surface Energy of Nano-SiO2 Particles for Oil/Gas Exploration and Development Based on the Density Functional Theory

Although nano SiO2 exhibits excellent application potential in the field of oil and gas exploration and development, such as drilling fluid, enhanced oil/gas recovery, etc., it is prone to agglomeration and loses its effectiveness due to the action of cations in saline environments of oil and gas reservoirs. Therefore, it is crucial to study the mechanism of the change in energy between nano SiO2 and cations for its industrial application. In this paper, the effect of cations (Na+, K+, Ca2+, and Mg2+) on the surface energy of nano SiO2 particles is investigated from the perspective of molecular motion and electronic change by density functional theory. The results are as follows: Due to the electrostatic interactions, cations can migrate towards the surface of nano SiO2 particles. During the migration process, monovalent cations are almost unaffected by water molecules, and they can be directly adsorbed on the surface by nano SiO2 particles. However, when divalent cations migrate from a distance to the surface of nano SiO2 particles, they can combine with water molecules to create an energy barrier, which can prevent them from moving forward. When divalent cations break through the energy barrier, the electronic kinetic energy between them and nano SiO2 particles changes more strongly, and the electrons carried by them are more likely to break through the edge of the atomic nucleus and undergo charge exchange with nano SiO2 particles. The change in interaction energy is more intense, which can further disrupt the configuration stability of nano SiO2. The interaction energy between cations and nano SiO2 particles mainly comes from electrostatic energy, followed by Van der Waals energy. From the degree of influence of four cations on nano SiO2 particles, the order from small to large is as follows: K+ < Na+ < Mg2+ < Ca2+. The research results can provide a theoretical understanding of the interaction between nano SiO2 particles and cations during the application of nano SiO2 in the field of oil and gas exploration and development.

Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR

Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rapid growth of databases of molecules for virtual screening and dramatic improvements in computational power have supported the emergence of a new field of QSAR applications that we term 'deep QSAR'. Marking a decade from the pioneering applications of deep QSAR to tasks involved in small-molecule drug discovery, we herein describe key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning and the application of deep QSAR models in structure-based virtual screening. We also reflect on the emergence of quantum computing, which promises to further accelerate deep QSAR applications and the need for open-source and democratized resources to support computer-aided drug design.

Applications and Potential of In Silico Approaches for Psychedelic Chemistry

Molecular-level investigations of the Central Nervous System have been revolutionized by the development of computational methods, computing power, and capacity advances. These techniques have enabled researchers to analyze large amounts of data from various sources, including genomics, in vivo, and in vitro drug tests. In this review, we explore how computational methods and informatics have contributed to our understanding of mental health disorders and the development of novel drugs for neurological diseases, with a special focus on the emerging field of psychedelics. In addition, the use of state-of-the-art computational methods to predict the potential of drug compounds and bioinformatic tools to integrate disparate data sources to create predictive models is also discussed. Furthermore, the challenges associated with these methods, such as the need for large datasets and the diversity of in vitro data, are explored. Overall, this review highlights the immense potential of computational methods and informatics in Central Nervous System research and underscores the need for continued development and refinement of these techniques and more inclusion of Quantitative Structure-Activity Relationships (QSARs).

Reaction mechanism - explored with the unified reaction valley approach

One of the ultimate goals of chemistry is to understand and manipulate chemical reactions, which implies the ability to monitor the reaction and its underlying mechanism at an atomic scale. In this article, we introduce the Unified Reaction Valley Approach (URVA) as a tool for elucidating reaction mechanisms, complementing existing computational procedures. URVA combines the concept of the potential energy surface with vibrational spectroscopy and describes a chemical reaction via the reaction path and the surrounding reaction valley traced out by the reacting species on the potential energy surface on their way from the entrance to the exit channel, where the products are located. The key feature of URVA is the focus on the curving of the reaction path. Moving along the reaction path, any electronic structure change of the reacting species is registered by a change in the normal vibrational modes spanning the reaction valley and their coupling with the path, which recovers the curvature of the reaction path. This leads to a unique curvature profile for each chemical reaction, with curvature minima reflecting minimal change and curvature maxima indicating the location of important chemical events such as bond breaking/formation, charge polarization and transfer, rehybridization, etc. A decomposition of the path curvature into internal coordinate components or other coordinates of relevance for the reaction under consideration, provides comprehensive insight into the origin of the chemical changes taking place. After giving an overview of current experimental and computational efforts to gain insight into the mechanism of a chemical reaction and presenting the theoretical background of URVA, we illustrate how URVA works for three diverse processes, (i) [1,3] hydrogen transfer reactions; (ii) α-keto-amino inhibitor for SARS-CoV-2 Mpro; (iii) Rh-catalyzed cyanation. We hope that this article will inspire our computational colleagues to add URVA to their repertoire and will serve as an incubator for new reaction mechanisms to be studied in collaboration with our experimental experts in the field.

How can machine learning and multiscale modeling benefit ocular drug development?

The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.

Computational Studies of Aflatoxin B1 (AFB1): A Review

Aflatoxin B₁ (AFB₁) exhibits the most potent mutagenic and carcinogenic activity among aflatoxins. For this reason, AFB₁ is recognized as a human group 1 carcinogen by the International Agency of Research on Cancer. Consequently, it is essential to determine its properties and behavior in different chemical systems. The chemical properties of AFB₁ can be explored using computational chemistry, which has been employed complementarily to experimental investigations. The present review includes in silico studies (semiempirical, Hartree-Fock, DFT, molecular docking, and molecular dynamics) conducted from the first computational study in 1974 to the present (2022). This work was performed, considering the following groups: (a) molecular properties of AFB₁ (structural, energy, solvent effects, ground and the excited state, atomic charges, among others); (b) theoretical investigations of AFB₁ (degradation, quantification, reactivity, among others); (c) molecular interactions with inorganic compounds (Ag⁺, Zn²⁺, and Mg²⁺); (d) molecular interactions with environmentally compounds (clays); and (e) molecular interactions with biological compounds (DNA, enzymes, cyclodextrins, glucans, among others). Accordingly, in this work, we provide to the stakeholder the knowledge of toxicity of types of AFB₁-derivatives, the structure-activity relationships manifested by the bonds between AFB₁ and DNA or proteins, and the types of strategies that have been employed to quantify, detect, and eliminate the AFB₁ molecule.

Quantum computing in pharma: A multilayer embedding approach for near future applications

Quantum computers are special purpose machines that are expected to be particularly useful in simulating strongly correlated chemical systems. The quantum computer excels at treating a moderate number of orbitals within an active space in a fully quantum mechanical manner. We present a quantum phase estimation calculation on F₂ in a (2,2) active space on Rigetti's Aspen-11 QPU. While this is a promising start, it also underlines the need for carefully selecting the orbital spaces treated by the quantum computer. In this work, a scheme for selecting such an active space automatically is described and simulated results obtained using both the quantum phase estimation (QPE) and variational quantum eigensolver (VQE) algorithms are presented and combined with a subtractive method to enable accurate description of the environment. The active occupied space is selected from orbitals localized on the chemically relevant fragment of the molecule, while the corresponding virtual space is chosen based on the magnitude of interactions with the occupied space calculated from perturbation theory. This protocol is then applied to two chemical systems of pharmaceutical relevance: the enzyme [Fe] hydrogenase and the photosenzitizer temoporfin. While the sizes of the active spaces currently amenable to a quantum computational treatment are not enough to demonstrate quantum advantage, the procedure outlined here is applicable to any active space size, including those that are outside the reach of classical computation.

Perspective on the Current State-of-the-Art of Quantum Computing for Drug Discovery Applications

Computational chemistry is an essential tool in the pharmaceutical industry. Quantum computing is a fast evolving technology that promises to completely shift the computational capabilities in many areas of chemical research by bringing into reach currently impossible calculations. This perspective illustrates the near-future applicability of quantum computation of molecules to pharmaceutical problems. We briefly summarize and compare the scaling properties of state-of-the-art quantum algorithms and provide novel estimates of the quantum computational cost of simulating progressively larger embedding regions of a pharmaceutically relevant covalent protein-drug complex involving the drug Ibrutinib. Carrying out these calculations requires an error-corrected quantum architecture that we describe. Our estimates showcase that recent developments on quantum phase estimation algorithms have dramatically reduced the quantum resources needed to run fully quantum calculations in active spaces of around 50 orbitals and electrons, from estimated over 1000 years using the Trotterization approach to just a few days with sparse qubitization, painting a picture of fast and exciting progress in this nascent field.

Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization

Artificial intelligence and specifically machine learning applications are nowadays used in a variety of scientific applications and cutting-edge technologies, where they have a transformative impact. Such an assembly of statistical and linear algebra methods making use of large data sets is becoming more and more integrated into chemistry and crystallization research workflows. This review aims to present, for the first time, a holistic overview of machine learning and cheminformatics applications as a novel, powerful means to accelerate the discovery of new crystal structures, predict key properties of organic crystalline materials, simulate, understand, and control the dynamics of complex crystallization process systems, as well as contribute to high throughput automation of chemical process development involving crystalline materials. We critically review the advances in these new, rapidly emerging research areas, raising awareness in issues such as the bridging of machine learning models with first-principles mechanistic models, data set size, structure, and quality, as well as the selection of appropriate descriptors. At the same time, we propose future research at the interface of applied mathematics, chemistry, and crystallography. Overall, this review aims to increase the adoption of such methods and tools by chemists and scientists across industry and academia.

Emerging Landscape of Computational Modeling in Pharmaceutical Development

Computational chemistry applications have become an integral part of the drug discovery workflow over the past 35 years. However, computational modeling in support of drug development has remained a relatively uncharted territory for a significant part of both academic and industrial communities. This review considers the computational modeling workflows for three key components of drug preclinical and clinical development, namely, process chemistry, analytical research and development, as well as drug product and formulation development. An overview of the computational support for each step of the respective workflows is presented. Additionally, in context of solid form design, special consideration is given to modern physics-based virtual screening methods. This covers rational approaches to polymorph, coformer, counterion, and solvent virtual screening in support of solid form selection and design.

BH9, a New Comprehensive Benchmark Data Set for Barrier Heights and Reaction Energies: Assessment of Density Functional Approximations and Basis Set Incompleteness Potentials

The calculation of accurate reaction energies and barrier heights is essential in computational studies of reaction mechanisms and thermochemistry. To assess methods regarding their ability to predict these two properties, high-quality benchmark sets are required that comprise a reasonably large and diverse set of organic reactions. Due to the time-consuming nature of both locating transition states and computing accurate reference energies for reactions involving large molecules, previous benchmark sets have been limited in scope, the number of reactions considered, and the size of the reactant and product molecules. Recent advances in coupled-cluster theory, in particular local correlation methods like DLPNO-CCSD(T), now allow the calculation of reaction energies and barrier heights for relatively large systems. In this work, we present a comprehensive and diverse benchmark set of barrier heights and reaction energies based on DLPNO-CCSD(T)/CBS called BH9. BH9 comprises 449 chemical reactions belonging to nine types common in organic chemistry and biochemistry. We examine the accuracy of DLPNO-CCSD(T) vis-a-vis canonical CCSD(T) for a subset of BH9 and conclude that, although there is a penalty in using the DLPNO approximation, the reference data are accurate enough to serve as a benchmark for density functional theory (DFT) methods. We then present two applications of the BH9 set. First, we examine the performance of several density functional approximations commonly used in thermochemical and mechanistic studies. Second, we assess our basis set incompleteness potentials regarding their ability to mitigate basis set incompleteness errors. The number of data points, the diversity of the reactions considered, and the relatively large size of the reactant molecules make BH9 the most comprehensive thermochemical benchmark set to date and a useful tool for the development and assessment of computational methods.

Artificial Intelligence and Quantum Computing as the Next Pharma Disruptors

Artificial intelligence (AI) consists of a synergistic assembly of enhanced optimization strategies with wide application in drug discovery and development, providing advanced tools for promoting cost-effectiveness throughout drug life cycle. Specifically, AI brings together the potential to improve drug approval rates, reduce development costs, get medications to patients faster, and help patients complying with their treatments. Accelerated pharmaceutical development and drug product approval rates can further benefit from the quantum computing (QC) technology, which will ultimately enable larger profits from patent-protected market exclusivity.Key pharma stakeholders are endorsing cutting-edge technologies based on AI and QC , covering drug discovery, preclinical and clinical development, and postapproval activities. Indeed, AI-QC applications are expected to become standard in the pharma operating model over the next 5-10 years. Generalizing scalability to larger pharmaceutical problems instead of specialization is now the main principle for transforming pharmaceutical tasks on multiple fronts, for which systematic and cost-effective solutions have benefited in areas such as molecular screening, synthetic pathway design, and drug discovery and development.The information generated by coupling the life cycle of drugs and AI and/or QC through data-driven analysis, neural network prediction, and chemical system monitoring will enable (1) better understanding of the complexity of process data, (2) streamlining the design of experiments, (3) discovering new molecular targets and materials, and also (4) planning or rethinking upcoming pharmaceutical challenges The power of AI-QC makes accessible a range of different pharmaceutical problems and their rationalization that have not been previously addressed due to a lack of appropriate analytical tools, demonstrating the breadth of potential applications of these emerging multidimensional approaches. In this context, creating the right AI-QC strategy often involves a steep learning path, especially given the embryonic stage of the industry development and the relative lack of case studies documenting success. As such, a comprehensive knowledge of the underlying pillars is imperative to extend the landscape of applications across the drug life cycle.The topics enclosed in this chapter will focus on AI-QC methods applied to drug discovery and development, with emphasis on the most recent advances in this field.

Artificial intelligence and automation in computer aided synthesis planning

In this perspective we deal with questions pertaining to the development of synthesis planning technologies over the course of recent years.