Over the past century, drug discoveryDrug discovery has evolved from a largely serendipitous process to a systematic program. Nevertheless, after a period of rapid advances in new “miracle drugs”, the pace of discovery of novel therapeutic molecules has not kept up with the increase in pharmaceutical R D spending. It now typically takes over a billion dollars to bring a new drug to market. Machine learning methods are now routinely used in the pharmaceutical industry to analyze the large amount of data generated by robotic high-throughput biological assays, leading to hopes for shortening of the drug discoveryDrug discovery pipeline. The advent of this data-rich era has given rise to a new data-driven, rather than the traditional hypothesis-driven paradigm in drug design. This trend is amplified by recent generative deep learning methods, which offer the promise of rapidly and cost-effectively generating new bioactive molecules within a desired range of properties. Application of machine learningMachine learning in drug design and informatics methods in materials discovery has been slower to take off, but has witnessed remarkable progress in recent years, with the availability of large materials databases, deep generative methods, robotic synthesis and characterization tools. This chapter reviews recent developments in the application of artificial intelligence and machine learning techniques to the design of novel materials and therapeutic drugs, highlights some ongoing concerns and anticipates possible future trends.

Applications of photochemistry are becoming very popular in modern-day life due to its operational simplicity, environmentally friendly and economically sustainable nature in comparison to thermochemistry. In particular photoinduced radical polymerisation (PRP) reactions are finding more biological applications and especially in the areas of dental restoration processes, tissue engineering and artificial bone generation. A type-II photoinitiator and co-initiator-promoted PRP turned out to be a cost-effective protocol, and herein we report the design and synthesis of a new efficient co-initiator for a PRP reaction via a barrierless sequential conjugate addition reaction. Experimental mechanistic observations have been further complemented by computational data. Time for newly synthesised 1,2-benzenedithiol (DTH) based co-initiator promoted polymerisation of urethane dimethacrylate (UDMA, 70 %) and triethylene glycol dimethacrylate (TEGDMA, 30 %) in presence of 450 nm LED (15 W) under the aerobic conditions is 38 seconds. Polymeric material has high glass transition temperature, improved mechanical strength (860 BHN) and longer in-depth polymerisation (3 cm).

Large collections of molecules (chemical libraries) are nowadays routinely screened in the process of designing drugs for specific ailments. Chemical and structural similarities between these molecules can be quantified using molecular descriptors, and these similarities can in turn be used to represent any chemical library as an undirected network called a chemical space network (CSN). Here we study different CSNs using conventional graph measures as well as random matrix theory (RMT). For the conventional graph measures, we focus on the average degree, average path length, graph diameter, degree assortativity, transitivity, average clustering coefficient and modularity. For the RMT analyses, we examine the eigenvalue spectra of adjacency matrices constructed from the molecular similarities for different CSNs, and examine their local fluctuation properties, contrasting them with the predictions of RMT. Changes in the conventional graph measures and RMT statistics with the network structure are examined for three different chemical libraries by varying the edge density (fraction of the actual to the maximum possible number of edges) of the networks. It is found that the assortativity among the conventional graph measures, and long-range fluctuation statistics of RMT in eigenvalue space respond to the changes in global network structure as well as the chemical space. We expect that this investigation of the network characteristics of different kinds of chemical libraries will provide guidance in the design of high-throughput screening libraries for different drug design applications.

Abstract A new strain of coronavirus known as severe acute respiratory syndrome (SARS) coronavirus-2 (SARS-CoV-2) is responsible for the current COVID-19 pandemic, which has not only affected the health of millions of individuals, but also caused severe socio-economic disruption. To curb the spread of the virus, various strategies have been employed to develop new drug therapy. Considering the pandemic condition, targeting the substrate binding site of main protease enzyme of SARS-CoV-2, which plays an important role in replication of the coronavirus, will be beneficial. Its high similarity with its predecessor virus’s main protease and dissimilarity with human protease makes it a promising target and will also facilitate drug repurposing. In this study, we have used Protein Encoded Shape Distributions (PESD) to calculate similarity between the ligand binding site of the SARS-CoV-2 main protease and other proteins. Similarity networks were constructed between these proteins using different distance metrics with edge density < 0.1 percent. Construction of low edge density protein-ligand binding site similarity networks helped in rational identification of the most similar ligand binding sites of proteins with the SARS-CoV-2 main protease. Based on this knowledge, a dataset of FDA approved drug molecules as well as experimental drugs was collected from the literature. These molecules were subjected to virtual screening through molecular docking against the SARS-CoV-2 main protease, followed by conventional molecular dynamics simulation, replica exchange molecular dynamics and binding free energy calculations. Based on these studies Q27458218 have been found to be suitable for repurposing as a SARS-CoV-2 main protease inhibitor.

Cancer cells frequently exhibit uncoupling of the glycolytic pathway from the TCA cycle (i.e., the "Warburg effect") and as a result, often become dependent on their ability to increase glutamine catabolism. The mitochondrial enzyme Glutaminase C (GAC) helps to satisfy this 'glutamine addiction' of cancer cells by catalyzing the hydrolysis of glutamine to glutamate, which is then converted to the TCA-cycle intermediate α-ketoglutarate. This makes GAC an intriguing drug target and spurred the molecules derived from bis-2-(5-phenylacetamido-1,3,4-thiadiazol-2-yl)ethyl sulfide (the so-called BPTES class of allosteric GAC inhibitors), including CB-839, which is currently in clinical trials. However, none of the drugs targeting GAC are yet approved for cancer treatment and their mechanism of action is not well understood. Here, we shed new light on the underlying basis for the differential potencies exhibited by members of the BPTES/CB-839 family of compounds, which could not previously be explained with standard cryo-cooled X-ray crystal structures of GAC bound to CB-839 or its analogs. Using an emerging technique known as serial room temperature crystallography, we were able to observe clear differences between the binding conformations of inhibitors with significantly different potencies. We also developed a computational model to further elucidate the molecular basis of differential inhibitor potency. We then corroborated the results from our modeling efforts using recently established fluorescence assays that directly read out inhibitor binding to GAC. Together, these findings should aid in future design of more potent GAC inhibitors with better clinical outlook.

Many cancer cells become dependent on glutamine metabolism to compensate for glycolysis being uncoupled from the TCA cycle. The mitochondrial enzyme Glutaminase C (GAC) satisfies this ‘glutamine addiction’ by catalyzing the first step in glutamine metabolism, making it an attractive drug target. Despite one of the allosteric inhibitors (CB-839) being in clinical trials, none of the drugs targeting GAC are approved for cancer treatment and their mechanism of action is not well understood. A major challenge has been the rational design of better drug candidates: standard cryo-cooled X-ray crystal structures of GAC bound to CB-839 and its analogs fail to explain their potency differences. Here, we address this problem by using an emerging technique, serial room temperature crystallography, which enabled us to observe clear differences between the binding conformations of inhibitors with significantly different potencies. A computational model was developed to further elucidate the molecular basis of inhibitor potency. We then corroborated the results from our modeling efforts by using recently established fluorescence assays that directly read-out inhibitor binding to GAC. Together, these findings provide new insights into the mechanisms used by a major class of allosteric GAC inhibitors and for the future rational design of more potent drug candidates.

While temporal considerations are of prime importance for chemical reactions, as well as for molecular stability, most chemical concepts (outside of the field of chemical kinetics) are not explicitly formulated on a diachronic basis (Earley in Found Chem 14:235, 2012). It will be argued here that a formulation explicitly incorporating temporal and epistemological considerations enables us to treat chemical reactions and chemical substances on ontologically equivalent terms, instead of assigning a more fundamental status to the latter. After all, in collision theory, a chemical substance is just a collision complex (a "resonance") that takes too long. How long qualifies as "too long", and "too long" in relation to what, are crucial questions that distinguish chemical substances from chemical reactions, and reversible reactions from irreversible ones, thereby introducing anthropocentric considerations into these distinctions. Too long for a lab chemist is very different from too long for an astrochemist studying chemical reactions between chemical substances in inter-stellar space on cosmological timescales. Examining several physical and chemical properties on the basis of which chemical substances are distinguished from one another, the role of temporal and anthropocentric considerations in defining molecular properties is emphasized. I conclude with some observations on the much-debated reduction of chemistry to other disciplines, arguing that such reduction depends on our aesthetic choices as to what kinds of observations demand explanation, and what kinds of explanation are acceptable.

Using density functional theory calculations, we study doping of a Cr, Mo, and W atom in boron clusters in the size range of 18-24 atoms and report the finding of metal atom encapsulated fullerene-like cage structures with 20 to 24 boron atoms in contrast to a fullerene-like structure of pure boron with 40 atoms. Our results show that bicapped drum-shaped structures are favored for neutral Cr@B$_{18}$, Mo@B$_{20}$, and W@B$_{20}$ clusters whereas a drum-shaped structure is preferred for neutral, cation, and anion of Mo@B$_{18}$ and W@B$_{18}$. Further, we find that B$_{20}$ is the smallest cage for Cr encapsulation, while B$_{22}$ is the smallest symmetric cage for Mo and W encapsulation and it is magic. Symmetric cage structures are also obtained for Mo@B$_{24}$ and W@B$_{24}$. A detailed analysis of the bonding character and molecular orbitals suggests that Cr@B$_{18}$, Cr@B$_{20}$, M@B$_{22}$ (M = Cr, Mo, and W) and M@B$_{24}$ (M = Mo and W) cages are stabilized with 18 $\pi$-bonded valence electrons whereas the drum-shaped M@B$_{18}$ (M = Mo and W) clusters are stabilized by 20 $\pi$-bonded valence electrons. Calculations with PBE0 functional in Gaussian 09 code show that in all cases of neutral clusters there is a large highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap. In some cases the lowest energy isomer of the charged clusters is different from the one for the neutral. We discuss the calculated infrared and Raman spectra for the neutral and cation clusters as well as the electronic structure of the anion clusters. Also we report results for isoelectronic anion and neutral clusters doped with V, Nb, and Ta which are generally similar to those obtained for Mo and W doped clusters. These results would be helpful to confirm the formation of these doped boron clusters experimentally.

Two different scoring functions, Hirshfeld fingerprint-based scoring (HFBS) and molecular operating environment (MOE), and the kernel energy method (KEM) along with counterpoise (CP)-corrected approach were used to estimate the binding energies of protein–ligand complexes and tested against a series of inhibitors of human aldose reductase enzyme. The new scoring function, HFBS, is based on Hirshfeld fingerprints, which are 2D histogram plots of the distances from the molecular Hirshfeld surface to the nearest atomic nuclei inside versus outside the surface and are highly sensitive to the immediate environment of the molecule. The Hirshfeld surface plotted over the ligand molecule helped to visualize the contacts with the active site residues and solvents, which were then taken into account for interaction energy calculations. Application of KEM-assisted CP-corrected approach facilitated an efficient way of calculating interaction energies in protein complex systems. Interaction energies calculated using MP2/6-31G(d) level of theory allowed us to rank the ligands by potency. We find that both the KEM-assisted CP-corrected interaction energies and the scoring functions used here predict comparable rankings for the strength of binding of the series of ligands as docked to the active site of the protein, which are also in good agreement with the experimental binding affinities in this case.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

We study the stability of drum-shaped transition metal (TM)-doped boron clusters, M@B with n = 14 and 16, and M = 3d, 4d, and Sd TM atom using ab initio calculations. Our results show that drum-shaped M@B-14 clusters are favored for M = Cr, Mn, Fe, Co, and Ni, while in other cases, open conical or bowl shaped structures become more favorable. The isoelectronic Ni@B-14 and Co@B-14(-) clusters have large highest occupied molecular orbital lowest unoccupied molecular orbital gaps and these are magic clusters. Their stability has been correlated with the occurrence of magic behavior with 24 valence electrons in a disk jellium model, while for Fe@B-14 case the drum structure is deformed and the stability occurs at 22 delocalized valence electrons. The bonding nature in these clusters has been studied by analyzing the electron density at bond and ring critical points, the Laplacian distribution of the electron density, the electron localization function, the source function, and electron localization-delocalization indices, all of which suggest two- and three-center a bonding within and between the two B7 rings, respectively, and hybridization between the TM d orbitals and the pi bonded molecular orbitals of the drum. The infrared and Raman spectra of these magic clusters show all real frequencies, suggesting the dynamical stability of the drum-shaped structures. There is a low frequency mode associated with the M atom. Results of the electronic spectra of the anion clusters are also presented that may help to identify these species in future experiments. Further, we discuss the stability of 24 delocalized valence electron systems Mn@B-16 anion, Fe@B-16, Co@B-16 cation, and other related clusters. Assembly of Co@B-14 clusters has been shown to stabilize a carbon nanotube-like nanotube of boron with Co atomic nanowire inside while a nanotube of boron with triangular network has been obtained with the assembly of Fe@ B-16 drum-shaped clusters. Both the nanotubes are metallic.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

Carbonyl-carbonyl n→π* interactions where a lone pair ( n ) of the oxygen atom of a carbonyl group is delocalized over the π * orbital of a nearby carbonyl group have attracted a lot of attention in recent years due to their ability to affect the 3D structure of small molecules, polyesters, peptides, and proteins. In this paper, we report the discovery of a “reciprocal” carbonyl-carbonyl interaction with substantial back and forth n→π* and π→π* electron delocalization between neighboring carbonyl groups. We have carried out experimental studies, analyses of crystallographic databases and theoretical calculations to show the presence of this interaction in both small molecules and proteins. In proteins, these interactions are primarily found in polyproline II (PPII) helices. As PPII are the most abundant secondary structures in unfolded proteins, we propose that these local interactions may have implications in protein folding.

Boron atomic clusters show several interesting and unusual size-dependent features due to the small covalent radius, electron deficiency, and higher coordination number of boron as compared to carbon. These include aromaticity and a diverse array of structures such as quasi-planar, ring or tubular shaped, and fullerene-like. In the present work, we have analyzed features of the computed electron density distributions of small boron clusters having up to 11 boron atoms, and investigated the effect of doping with C, P, Al, Si, and Zn atoms on their structural and physical properties, in order to understand the bonding characteristics and discern trends in bonding and stability. We find that in general there are covalent bonds as well as delocalized charge distribution in these clusters. We associate the strong stability of some of these planar/quasiplanar disc-type clusters with the electronic shell closing with effectively twelve delocalized valence electrons using a disc-shaped jellium model. B-9(-), B-10, B7P, and B8Si, in particular, are found to be exceptional with very large gaps between the highest occupied molecular orbital and the lowest unoccupied molecular orbital, and these are suggested to be magic clusters.

To facilitate the development of new polymeric materials, we report the development of new heuristic models to predict the dielectric constant, band gap, dielectric loss tangent, and glass transition temperatures for organic polymers. A new set of features called infinite chain descriptors (ICDs) was designed and developed especially to characterize organic polymers, utilizing methods with minimal dependence on pre-defined fragment libraries. Machine learning models were built for the aforementioned properties incorporating best practices in the field such as objective feature selection, cross-validation and external test sets. All models produced in this study showed good performance in prediction. A web tool has been developed and has been made available that supports the input of novel structures. (C) 2016 Wiley Periodicals, Inc.

Topological properties of chemical library networks, such as the average clustering coefficient, average path length, and existence of hubs, can serve as indicators to describe the inherent complexities of chemical libraries. We have used Diversity-Oriented Synthesis (DOS) and Focussed Libraries to investigate the appearance of scale-free properties and absence of small-world behavior in chemical libraries. DOS aims to elicit structural complexity in small compounds with respect to skeleton, functional groups, appendages and stereochemistry. Complexity here indicates incorporation of \(\hbox {sp}^{3}\) carbons, hydrogen bond acceptors and donors in the molecule. Biological studies have shown how structural complexity enhances the interaction of molecules with complex biological macromolecules. In contrast, Focussed Libraries concentrate on specific scaffolds against a specific biological target. We have quantified the diversity in several DOS and Focussed Libraries based on properties of similarity and dissimilarity threshold networks formed from them. Similarity and dissimilarity networks were generated from diverse chemical libraries at various Tanimoto similarity coefficients (\(\hbox {t}_{\mathrm{c}})\) using FP2 and MACCS fingerprints. The dissimilarity networks at very low \(\hbox {t}_{\mathrm{c}}\) threshold led to the absence of small-world behaviors, as evidenced by low average clustering coefficient and high average path length in comparison to Erdös–Renyi networks. Dissimilarity networks exhibit scale free topology as evidenced by a power law degree distribution. The similarity networks at high \(\hbox {t}_{\mathrm{c}}\) threshold have shown high clustering coefficients and low average path lengths, without the appearance of hubs. Combining dissimilarity and similarity threshold graphs revealed assortative and dissortative behaviors in the DOS libraries, leading to the conclusion that the vertices of the dissimilarity communities are more likely to share similarity edges, but it is quite unlikely for the vertices in a similarity community to share dissimilarity edges. We propose a simple and convenient diversity quantification tool, QuaLDI (Quantitative Library Diversity Index) to quantify the diversity in DOS and Focussed libraries. We anticipate that these topological properties can be used as descriptors to quantify the diversity in chemical libraries before proceeding for synthesis.