Natural products and their molecular frameworks have a long tradition as valuable starting points for medicinal chemistry and drug discovery. Recently, there has been a revitalization of interest in the inclusion of these chemotypes in compound collections for screening and achieving selective target modulation. Here we discuss natural-product-inspired drug discovery with a focus on recent advances in the design of synthetically tractable small molecules that mimic nature's chemistry. We highlight the potential of innovative computational tools in processing structurally complex natural products to predict their macromolecular targets and attempt to forecast the role that natural-product-derived fragments and fragment-like natural products will play in next-generation drug discovery.

Counting on natural products for drug design

Metal ions play significant roles in numerous fields including chemistry, geochemistry, biochemistry, and materials science. With computational tools increasingly becoming important in chemical research, methods have emerged to effectively face the challenge of modeling metal ions in the gas, aqueous, and solid phases. Herein, we review both quantum and classical modeling strategies for metal ion-containing systems that have been developed over the past few decades. This Review focuses on classical metal ion modeling based on unpolarized models (including the nonbonded, bonded, cationic dummy atom, and combined models), polarizable models (e.g., the fluctuating charge, Drude oscillator, and the induced dipole models), the angular overlap model, and valence bond-based models. Quantum mechanical studies of metal ion-containing systems at the semiempirical, ab initio, and density functional levels of theory are reviewed as well with a particular focus on how these methods inform classical modeling efforts. F...

Metal Ion Modeling Using Classical Mechanics

In silico approaches and tools for the prediction of drug metabolism and fate: A review.

The latest findings on the role played by human LDH5 (hLDH5) in the promotion of glycolysis in invasive tumor cells indicates that this enzyme subtype is a promising therapeutic target for invasive cancer. Compounds able to selectively inhibit hLDH5 hold promise for the cure of neoplastic diseases. hLDH5 has so far been a rather unexplored target, since its importance in the promotion of cancer progression has been neglected for decades. This enzyme should also be considered as a challenging target due the high polar character (mostly cationic) of its ligand cavity. Recently, significant progresses have been reached with small-molecule inhibitors of hLDH5 displaying remarkable potencies and selectivities. This review provides an overview of the newly developed hLDH5 inhibitors. The roles of hLDH isoforms will be briefly discussed, and then the inhibitors will be grouped into chemical classes. Furthermore, general pharmacophore features will be emphasized throughout the structural subgroups analyzed.

Small-molecule inhibitors of human LDH5

Sirtuins are a family of NAD+-dependent enzymes that was proposed to control organismal life span about a decade ago. While such role of sirtuins is now debated, mounting evidence involves these enzymes in numerous physiological processes and disease conditions, including metabolism, nutritional behavior, circadian rhythm, but also inflammation and cancer. SIRT1, SIRT2, SIRT3, SIRT6, and SIRT7 have all been linked to carcinogenesis either as tumor suppressor or as cancer promoting proteins. Here, we review the biological rationale for the search of sirtuin inhibitors and activators for treating cancer and the experimental approaches to their identification.

Rejuvenating Sirtuins: The Rise of a New Family of Cancer Drug Targets

Cancer remains a fundamental burden to public health despite substantial efforts aimed at developing effective chemotherapeutics and significant advances in chemotherapeutic regimens. The major challenge in anti-cancer drug design is to selectively target cancer cells with high specificity. Research into treating malignancies by targeting altered metabolism in cancer cells is supported by computational approaches, which can take a leading role in identifying candidate targets for anti-cancer therapy as well as assist in the discovery and optimisation of anti-cancer agents. Natural products appear to have privileged structures for anti-cancer drug development and the bulk of this particularly valuable chemical space still remains to be explored. In this review we aim to provide a comprehensive overview of current strategies for computer-guided anti-cancer drug development. We start with a discussion of state-of-the art bioinformatics methods applied to the identification of novel anti-cancer targets, including machine learning techniques, the Connectivity Map and biological network analysis. This is followed by an extensive survey of molecular modelling and cheminformatics techniques employed to develop agents targeting proteins involved in the glycolytic, lipid, NAD+, mitochondrial (TCA cycle), amino acid and nucleic acid metabolism of cancer cells. A dedicated section highlights the most promising strategies to develop anti-cancer therapeutics from natural products and the role of metabolism and some of the many targets which are under investigation are reviewed. Recent success stories are reported for all the areas covered in this review. We conclude with a brief summary of the most interesting strategies identified and with an outlook on future directions in anti-cancer drug development.

Anti-cancer drug development: computational strategies to identify and target proteins involved in cancer metabolism.

Software for Metabolism Prediction

Inhibition of the 3' processing step of HIV-1 integrase by small molecule inhibitors is one of the most promising strategies for the treatment of AIDS. Using a support vector machine (SVM) approach, we developed six classification models for predicting 3'P inhibitors. The models are based on up to 48 selected molecular descriptors and a comprehensive data set of 1253 molecules, with measured activities ranging from nanomolar to micromolar IC50 values. Model B2, the most robust SVM model, obtains a prediction accuracy, sensitivity, specificity and Matthews correlation coefficient (MCC) of 93 %, 81 %, 94 % and 0.67 on the test set, respectively. The presence of hydrogen bonding features and hydrophilicity in general were identified as key determinants of inhibitory activity. Further important properties include molecular refractivity, π atom charge, total charge, lone pair electronegativity, and effective atom polarizability. Comparative fragment-based analysis of the active and inactive molecules corroborated these observations and revealed several characteristic structural elements of 3'P inhibitors. The models built in this study can be obtained from the authors.

Support Vector Machine (SVM) Models for Predicting Inhibitors of the 3′ Processing Step of HIV‐1 Integrase

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Anti-cancer drug development: computational strategies to identify and target proteins involved in cancer metabolism.

Software for Metabolism Prediction

Support Vector Machine (SVM) Models for Predicting Inhibitors of the 3′ Processing Step of HIV‐1 Integrase