Accelerating high-throughput virtual screening through molecular pool-based active learning

Structure-based virtual screening is an important tool in early stage drug discovery that scores the interactions between a target protein and candidate ligands. As virtual libraries continue to grow (in excess of 10⁸ molecules), so too do the resources necessary to conduct exhaustive virtual screening campaigns on these libraries. However, Bayesian optimization techniques, previously employed in other scientific discovery problems, can aid in their exploration: a surrogate structure–property relationship model trained on the predicted affinities of a subset of the library can be applied to the remaining library members, allowing the least promising compounds to be excluded from evaluation. In this study, we explore the application of these techniques to computational docking datasets and assess the impact of surrogate model architecture, acquisition function, and acquisition batch size on optimization performance. We observe significant reductions in computational costs; for example, using a directed-message passing neural network we can identify 94.8% or 89.3% of the top-50 000 ligands in a 100M member library after testing only 2.4% of candidate ligands using an upper confidence bound or greedy acquisition strategy, respectively. Such model-guided searches mitigate the increasing computational costs of screening increasingly large virtual libraries and can accelerate high-throughput virtual screening campaigns with applications beyond docking.

Bayesian optimization can accelerate structure-based virtual screening campaigns by minimizing the total number of simulations performed while still identifying the vast majority of computational hits.     

Engineering entangled photon pairs with metal–organic frameworks

The discovery and design of new materials with competitive optical frequency conversion efficiencies can accelerate the development of scalable photonic quantum technologies. Metal–organic framework (MOF) crystals without inversion symmetry have shown potential for these applications, given their nonlinear optical properties and the combinatorial number of possibilities for MOF self-assembly. In order to accelerate the discovery of MOF materials for quantum optical technologies, scalable computational assessment tools are needed. We develop a multi-scale methodology to study the wavefunction of entangled photon pairs generated by selected non-centrosymmetric MOF crystals via spontaneous parametric down-conversion (SPDC). Starting from an optimized crystal structure, we predict the shape of the G⁽²⁾ intensity correlation function for coincidence detection of the entangled pairs, produced under conditions of collinear type-I phase matching. The effective nonlinearities and photon pair correlation times obtained are comparable to those available with inorganic crystal standards. Our work thus provides fundamental insights into the structure–property relationships for entangled photon generation with metal–organic frameworks, paving the way for the automated discovery of molecular materials for optical quantum technology.

The discovery and design of new materials with competitive optical frequency conversion efficiencies can accelerate the development of scalable photonic quantum technologies.     

Can we predict materials that can be synthesised?

The discovery of materials is an important element in the development of new technologies and abilities that can help humanity tackle many challenges. Materials discovery is frustratingly slow, with the large time and resource cost often providing only small gains in property performance. Furthermore, researchers are unwilling to take large risks that they will only know the outcome of months or years later. Computation is playing an increasing role in allowing rapid screening of large numbers of materials from vast search space to identify promising candidates for laboratory synthesis and testing. However, there is a problem, in that many materials computationally predicted to have encouraging properties cannot be readily realised in the lab. This minireview looks at how we can tackle the problem of confirming that hypothetical materials are synthetically realisable, through consideration of all the stages of the materials discovery process, from obtaining the components, reacting them to a material in the correct structure, through to processing into a desired form. In an ideal world, a material prediction would come with an associated ‘recipe’ for the successful laboratory preparation of the material. We discuss the opportunity to thus prevent wasted effort in experimental discovery programmes, including those using automation, to accelerate the discovery of novel materials.

Materials discovery is a crucial yet experimentally slow and wasteful process. We discuss how discovery can be accelerated by focusing on making predictions that are synthetically realisable.     

Stable and easily available sulfide surrogates allow a stereoselective activation of alcohols

Isothiouronium salts are easily accessible and stable compounds. Herein, we report their use as versatile deoxasulfenylating agents enabling a stereoselective, thiol-free protocol for synthesis of thioethers from alcohols. The method is simple, scalable and tolerates a broad range of functional groups otherwise incompatible with other methods. Late-stage modification of several pharmaceuticals provides access to multiple analogues of biologically relevant molecules. Performed experiments give insight into the reaction mechanism.

A simple and scalable method for stereoselective synthesis of thioethers directly from alcohols using isothiouronium salts is presented. The utility of this thiol-free reaction was exemplified by late-stage modification of complex molecules.     

Machine-enabled inverse design of inorganic solid materials: promises and challenges

Developing high-performance advanced materials requires a deeper insight and search into the chemical space. Until recently, exploration of materials space using chemical intuitions built upon existing materials has been the general strategy, but this direct design approach is often time and resource consuming and poses a significant bottleneck to solve the materials challenges of future sustainability in a timely manner. To accelerate this conventional design process, inverse design, which outputs materials with pre-defined target properties, has emerged as a significant materials informatics platform in recent years by leveraging hidden knowledge obtained from materials data. Here, we summarize the latest progress in machine-enabled inverse materials design categorized into three strategies: high-throughput virtual screening, global optimization, and generative models. We analyze challenges for each approach and discuss gaps to be bridged for further accelerated and rational data-driven materials design.

The grand challenge of materials science, discovery of novel materials with target properties, can be greatly accelerated by machine-learned inverse design strategies.     

Polymorph exploration of bismuth stannate using first-principles phonon mode mapping

Accurately modelling polymorphism in crystalline solids remains a key challenge in computational chemistry. In this work, we apply a theoretically-rigorous phonon mode-mapping approach to understand the polymorphism in the ternary metal oxide Bi₂Sn₂O₇. Starting from the high-temperature cubic pyrochlore aristotype, we systematically explore the structural potential-energy surface and recover the two known low-temperature phases alongside three new metastable phases, together with the transition pathways connecting them. This first-principles lattice-dynamics method is completely general and provides a practical means to identify and characterise the stable polymorphs and phase transitions in materials with complex crystal structures.

Using a phonon mode-mapping approach, we recover the known experimental phases of the ternary oxide Bi₂Sn₂O₇ and identify three new metastable phases, highlighting the utility of the method for polymorph prediction on many other complex materials.     

Evolutionary chemical space exploration for functional materials: computational organic semiconductor discovery

Computational methods, including crystal structure and property prediction, have the potential to accelerate the materials discovery process by enabling structure prediction and screening of possible molecular building blocks prior to their synthesis. However, the discovery of new functional molecular materials is still limited by the need to identify promising molecules from a vast chemical space. We describe an evolutionary method which explores a user specified region of chemical space to identify promising molecules, which are subsequently evaluated using crystal structure prediction. We demonstrate the methods for the exploration of aza-substituted pentacenes with the aim of finding small molecule organic semiconductors with high charge carrier mobilities, where the space of possible substitution patterns is too large to exhaustively search using a high throughput approach. The method efficiently explores this large space, typically requiring calculations on only ∼1% of molecules during a search. The results reveal two promising structural motifs: aza-substituted naphtho[1,2-a]anthracenes with reorganisation energies as low as pentacene and a series of pyridazine-based molecules having both low reorganisation energies and high electron affinities.

Evolutionary optimisation and crystal structure prediction are used to explore chemical space for molecular organic semiconductors.     

Effect of curvature and placement of donor and acceptor units in cycloparaphenylenes: a computational study

Cycloparaphenylenes have promise as novel fluorescent materials. However, shifting their fluorescence beyond 510 nm is difficult. Herein, we computationally explore the effect of incorporating electron accepting and electron donating units on CPP photophysical properties at the CAM-B3LYP/6-311G** level. We demonstrate that incorporation of donor and acceptor units may shift the CPP fluorescence as far as 1193 nm. This computational work directs the synthesis of bright red-emitting CPPs. Furthermore, the nanohoop architecture allows for interrogation of strain effects on common conjugated polymer donor and acceptor units. Strain results in a bathochromic shift versus linear variants, demonstrating the value of using strain to push the limits of low band gap materials.

Computational studies reveal near-IR emitting nanohoops and the effect of strain on donor and acceptor units used in conjugated polymers.     

One class classification as a practical approach for accelerating π–π co-crystal discovery

The implementation of machine learning models has brought major changes in the decision-making process for materials design. One matter of concern for the data-driven approaches is the lack of negative data from unsuccessful synthetic attempts, which might generate inherently imbalanced datasets. We propose the application of the one-class classification methodology as an effective tool for tackling these limitations on the materials design problems. This is a concept of learning based only on a well-defined class without counter examples. An extensive study on the different one-class classification algorithms is performed until the most appropriate workflow is identified for guiding the discovery of emerging materials belonging to a relatively small class, that being the weakly bound polyaromatic hydrocarbon co-crystals. The two-step approach presented in this study first trains the model using all the known molecular combinations that form this class of co-crystals extracted from the Cambridge Structural Database (1722 molecular combinations), followed by scoring possible yet unknown pairs from the ZINC15 database (21 736 possible molecular combinations). Focusing on the highest-ranking pairs predicted to have higher probability of forming co-crystals, materials discovery can be accelerated by reducing the vast molecular space and directing the synthetic efforts of chemists. Further on, using interpretability techniques a more detailed understanding of the molecular properties causing co-crystallization is sought after. The applicability of the current methodology is demonstrated with the discovery of two novel co-crystals, namely pyrene-6H-benzo[c]chromen-6-one (1) and pyrene-9,10-dicyanoanthracene (2).

Machine learning using one class classification on a database of existing co-crystals enables the identification of co-formers which are likely to form stable co-crystals, resulting in the synthesis of two co-crystals of polyaromatic hydrocarbons.     

Is it time for biocatalysis in fragment-based drug discovery?

The use of biocatalysts for fragment-based drug discovery has yet to be fully investigated, despite the promise enzymes hold for the synthesis of poly-functional, non-protected small molecules. Here we analyze products of the biocatalysis literature to demonstrate the potential for not only fragment generation, but also the enzyme-mediated elaboration of these fragments. Our analysis demonstrates that biocatalytic products can readily populate 3D chemical space, offering diverse catalytic approaches to help generate new, bioactive molecules.

This perspective discusses how biocatalysis could play an important role in the future fragment-based drug discovery.     

Relating atomic energy,radius and electronegativity through compression

Trends in atomic properties are well-established tools for guiding the analysis and discovery of materials. Here, we show how compression can reveal a long sought-after connection between two central chemical concepts – van-der-Waals (vdW) radii and electronegativity – and how these relate to the driving forces behind chemical and physical transformations.

Compression is used to derive a long sought-after connection between two central chemical concepts – van-der-Waals (vdW) radii and electronegativity – and how these relate to the driving forces behind chemical and physical transformations.     

Foldable semi-ladder polymers: novel aggregation behavior and high-performance solution-processed organic light-emitting transistors

A critical issue in developing high-performance organic light-emitting transistors (OLETs) is to balance the trade-off between charge transport and light emission in a semiconducting material. Although traditional materials for organic light-emitting diodes (OLEDs) or organic field-effect transistors (OFETs) have shown modest performance in OLET devices, design strategies towards high-performance OLET materials and the crucial structure–performance relationship remain unclear. Our research effort in developing cross-conjugated weak acceptor-weak donor copolymers for luminescent properties lead us to an unintentional discovery that these copolymers form coiled foldamers with intramolecular H-aggregation, leading to their exceptional OLET properties. An impressive external quantum efficiency (EQE) of 6.9% in solution-processed multi-layer OLET devices was achieved.

Coiled foldamers with intramolecular H-aggregation in semi-ladder copolymers lead towards the highest EQE of 6.9% in solution-processed multi-layer OLETs.     

Computational strategy for intrinsically disordered protein ligand design leads to the discovery of p53 transactivation domain I binding compounds that activate the p53 pathway

Intrinsically disordered proteins or intrinsically disordered regions (IDPs) have gained much attention in recent years due to their vital roles in biology and prevalence in various human diseases. Although IDPs are perceived as attractive therapeutic targets, rational drug design targeting IDPs remains challenging because of their conformational heterogeneity. Here, we propose a hierarchical computational strategy for IDP drug virtual screening (IDPDVS) and applied it in the discovery of p53 transactivation domain I (TAD1) binding compounds. IDPDVS starts from conformation sampling of the IDP target, then it combines stepwise conformational clustering with druggability evaluation to identify potential ligand binding pockets, followed by multiple docking screening runs and selection of compounds that can bind multi-conformations. p53 is an important tumor suppressor and restoration of its function provides an opportunity to inhibit cancer cell growth. TAD1 locates at the N-terminus of p53 and plays key roles in regulating p53 function. No compounds that directly bind to TAD1 have been reported due to its highly disordered structure. We successfully used IDPDVS to identify two compounds that bind p53 TAD1 and restore wild-type p53 function in cancer cells. Our study demonstrates that IDPDVS is an efficient strategy for IDP drug discovery and p53 TAD1 can be directly targeted by small molecules.

A hierarchical computational strategy for IDP drug virtual screening (IDPDVS) was proposed and successfully applied to identify compounds that bind p53 TAD1 and restore wild-type p53 function in cancer cells.     

Mechanochemical generation of acid-degradable poly(enol ether)s

In an effort to develop polymers that can undergo extensive backbone degradation in response to mechanical stress, we report a polymer system that is hydrolytically stable but unmasks easily hydrolysable enol ether backbone linkages when force is applied. These polymers were synthesized by ring-opening metathesis polymerization (ROMP) of a novel mechanophore monomer consisting of cyclic ether fused bicyclohexene. Hydrogenation of the resulting polymers led to significantly enhanced thermal stability (T_d > 400 °C) and excellent resistance toward acidic or basic conditions. Solution ultrasonication of the polymers resulted in up to 65% activation of the mechanophore units and conversion to backbone enol ether linkages, which then allowed facile degradation of the polymers to generate small molecule or oligomeric species under mildly acidic conditions. We also achieved solid-state mechano-activation and polymer degradation via grinding the solid polymer. Force-induced hydrolytic polymer degradability can enable materials that are stable under force-free conditions but readily degrade under stress. Facile degradation of mechanically activated polymechanophores also facilitates the analysis of mechanochemical products.

A mechanically responsive polymer system that is hydrolytically stable without stress, but unmasks enol ether backbone linkages under force to allow facile hydrolytic degradation.     

Predicting the chemical reactivity of organic materials using a machine-learning approach

Stability and compatibility between chemical components are essential parameters that need to be considered in the selection of functional materials in configuring a system. In configuring devices such as batteries or solar cells, not only the functionality of individual constituting materials such as electrodes or electrolyte but also an appropriate combination of materials which do not undergo unwanted side reactions is critical in ensuring their reliable performance in long-term operation. While the universal theory that can predict the general chemical reactivity between materials is long awaited and has been the subject of studies with a rich history, traditional ways proposed to date have been mostly based on simple electronic properties of materials such as electronegativity, ionization energy, electron affinity and hardness/softness, and could be applied to only a small group of materials. Moreover, prediction has often been far from accurate and has failed to offer general implications; thus it was practically inadequate as a selection criterion from a large material database, i.e. data-driven material discovery. Herein, we propose a new model for predicting the general reactivity and chemical compatibility among a large number of organic materials, realized by a machine-learning approach. As a showcase, we demonstrate that our new implemented model successfully reproduces previous experimental results reported on side-reactions occurring in lithium–oxygen electrochemical cells. Furthermore, the mapping of chemical stability among more than 90 available electrolyte solvents and the representative redox mediators is realized by this approach, presenting an important guideline in the development of stable electrolyte/redox mediator couples for lithium–oxygen batteries.

Stability and compatibility between chemical components are essential parameters that need to be considered in the selection of functional materials in configuring a system.     

Exploring the self-assembly of dumbbell-shaped polyoxometalate hybrids,from molecular building units to nanostructured soft materials

The formation of hierarchical nanostructures using preformed dumbbell-like species made of covalent organic–inorganic polyoxometalate (POM)-based hybrids is herein described. In this system, the presence of charged subunits (POM, metal linkers, and counter ions) in the complex molecular architecture can drive their aggregation, which results from a competition between the solvation energy of the discrete species and intermolecular electrostatic interactions. We show that the nature of the POM and the charge of the metal linker are key parameters for the hierarchical nanoorganization. The experimental findings were corroborated with a computational investigation combining DFT and molecular dynamics simulation methods, which outlines the importance of solvation of the counter ion and POM/counter ion association in the aggregation process. The dumbbell-like species can also form gels, in the presence of a poorer solvent, displaying similar nanoorganization of the aggregates. We show that starting from the designed molecular building units whose internal charges can be controlled by redox trigger we can achieve their implementation into soft nanostructured materials through the control of their supramolecular organization.

The formation of hierarchical nanostructures using supramolecular dumbbell-like species made of organic–inorganic polyoxometalate-based hybrids is investigated by combination of SAXS and computational methods.     

Understanding the reactivity of polycyclic aromatic hydrocarbons and related compounds

This perspective article summarizes recent applications of the combination of the activation strain model of reactivity and the energy decomposition analysis methods to the study of the reactivity of polycyclic aromatic hydrocarbons and related compounds such as cycloparaphenylenes, fullerenes and doped systems. To this end, we have selected representative examples to highlight the usefulness of this relatively novel computational approach to gain quantitative insight into the factors controlling the so far not fully understood reactivity of these species. Issues such as the influence of the size and curvature of the system on the reactivity are covered herein, which is crucial for the rational design of novel compounds with tuneable applications in different fields such as materials science or medicinal chemistry.

This perspective article summarizes recent applications of the combined activation strain model of reactivity and the energy decomposition analysis methods to the study of the reactivity of polycyclic aromatic hydrocarbons and related compounds.     

Atomistic fibrillar architectures of polar prion-inspired heptapeptides

This article provides the computational prediction of the atomistic architectures resulting from self-assembly of the polar heptapeptide sequences NYNYNYN, SYSYSYS and GYGYGYG. Using a combination of molecular dynamics and a newly developed tool for non-covalent interaction analysis, we uncover the properties of a new class of bionanomaterials, including hydrogen-bonded polar zippers, and the relationship between peptide composition, fibril geometry and weak interaction networks. Our results, corroborated by experimental observations, provide the basis for the rational design of prion-inspired nanomaterials.

This article provides the computational prediction of the atomistic architectures resulting from self-assembly of the polar heptapeptide sequences NYNYNYN, SYSYSYS and GYGYGYG.     

DP4-AI automated NMR data analysis: straight from spectrometer to structure

A robust system for automatic processing and assignment of raw ¹³C and ¹H NMR data DP4-AI has been developed and integrated into our computational organic molecule structure elucidation workflow. Starting from a molecular structure with undefined stereochemistry or other structural uncertainty, this system allows for completely automated structure elucidation. Methods for NMR peak picking using objective model selection and algorithms for matching the calculated ¹³C and ¹H NMR shifts to peaks in noisy experimental NMR data were developed. DP4-AI achieved a 60-fold increase in processing speed, and near-elimination of the need for scientist time, when rigorously evaluated using a challenging test set of molecules. DP4-AI represents a leap forward in NMR structure elucidation and a step-change in the functionality of DP4. It enables high-throughput analyses of databases and large sets of molecules, which were previously impossible, and paves the way for the discovery of new structural information through machine learning. This new functionality has been coupled with an intuitive GUI and is available as open-source software at https://github.com/KristapsE/DP4-AI.

A robust system for automatic processing and assignment of raw ¹³C and ¹H NMR data DP4-AI has been developed and integrated into our computational organic molecule structure elucidation workflow.     

The non-adiabatic nanoreactor: towards the automated discovery of photochemistry

The ab initio nanoreactor has previously been introduced to automate reaction discovery for ground state chemistry. In this work, we present the nonadiabatic nanoreactor, an analogous framework for excited state reaction discovery. We automate the study of nonadiabatic decay mechanisms of molecules by probing the intersection seam between adiabatic electronic states with hyper-real metadynamics, sampling the branching plane for relevant conical intersections, and performing seam-constrained path searches. We illustrate the effectiveness of the nonadiabatic nanoreactor by applying it to benzene, a molecule with rich photochemistry and a wide array of photochemical products. Our study confirms the existence of several types of S₀/S₁ and S₁/S₂ conical intersections which mediate access to a variety of ground state stationary points. We elucidate the connections between conical intersection energy/topography and the resulting photoproduct distribution, which changes smoothly along seam space segments. The exploration is performed with minimal user input, and the protocol requires no previous knowledge of the photochemical behavior of a target molecule. We demonstrate that the nonadiabatic nanoreactor is a valuable tool for the automated exploration of photochemical reactions and their mechanisms.

The nonadiabatic nanoreactor is a tool for automated photochemical reaction discovery that extensively explores intersection seams and links conical intersections to photoproduct distributions.