Structure is beauty, but not always truth.

Structural biology, as powerful as it is, can be misleading. We highlight four fundamental challenges: interpreting raw experimental data; accounting for motion; addressing the misleading nature of in vitro structures; and unraveling interactions between drugs and "anti-targets." Overcoming these challenges will amplify the impact of structural biology on drug discovery.

Principles and functions of condensate modifying drugs.

Biomolecular condensates are compartmentalized communities of biomolecules, which unlike traditional organelles, are not enclosed by membranes. Condensates play roles in diverse cellular processes, are dysfunctional in many disease states, and are often enriched in classically "undruggable" targets. In this review, we provide an overview for how drugs can modulate condensate structure and function by phenotypically classifying them as dissolvers (dissolve condensates), inducers (induce condensates), localizers (alter localization of the specific condensate community members) or morphers (alter the physiochemical properties). We discuss the growing list of bioactive molecules that function as condensate modifiers (c-mods), including small molecules, oligonucleotides, and peptides. We propose that understanding mechanisms of condensate perturbation of known c-mods will accelerate the discovery of a new class of therapies for difficult-to-treat diseases.

Modulating biomolecular condensates: a novel approach to drug discovery.

In the past decade, membraneless assemblies known as biomolecular condensates have been reported to play key roles in many cellular functions by compartmentalizing specific proteins and nucleic acids in subcellular environments with distinct properties. Furthermore, growing evidence supports the view that biomolecular condensates often form by phase separation, in which a single-phase system demixes into a two-phase system consisting of a condensed phase and a dilute phase of particular biomolecules. Emerging understanding of condensate function in normal and aberrant cellular states, and of the mechanisms of condensate formation, is providing new insights into human disease and revealing novel therapeutic opportunities. In this Perspective, we propose that such insights could enable a previously unexplored drug discovery approach based on identifying condensate-modifying therapeutics (c-mods), and we discuss the strategies, techniques and challenges involved.

What Makes a Great Medicinal Chemist? A Personal Perspective.

Although it is extremely challenging to invent new medicines, I have observed that certain behaviors seem to be commonly found among successful medicinal chemists. Those who exhibit most of these character traits are far more likely to bring new drugs into the clinic and onto the market. And, importantly, organizations that encourage these behaviors are far more likely to be successful. These traits can be broken into two categories: "general" and "discipline-specific". General traits are those that are common to all great scientists, while the discipline-specific ones are more specialized behaviors relevant to the medicinal chemistry enterprise. I describe these traits, and include some specific examples for each of the medicinal chemistry characteristics that I hope will be illustrative. While success in drug discovery is never guaranteed, I believe that embracing and encouraging these behaviors increase the probability of a successful outcome.

Preclinical activity of VX-787, a first-in-class, orally bioavailable inhibitor of the influenza virus polymerase PB2 subunit.

VX-787 is a novel inhibitor of influenza virus replication that blocks the PB2 cap-snatching activity of the influenza viral polymerase complex. Viral genetics and X-ray crystallography studies provide support for the idea that VX-787 occupies the 7-methyl GTP (m(7)GTP) cap-binding site of PB2. VX-787 binds the cap-binding domain of the PB2 subunit with a KD (dissociation constant) of 24 nM as determined by isothermal titration calorimetry (ITC). The cell-based EC50 (the concentration of compound that ensures 50% cell viability of an uninfected control) for VX-787 is 1.6 nM in a cytopathic effect (CPE) assay, with a similar EC50 in a viral RNA replication assay. VX-787 is active against a diverse panel of influenza A virus strains, including H1N1pdm09 and H5N1 strains, as well as strains with reduced susceptibility to neuraminidase inhibitors (NAIs). VX-787 was highly efficacious in both prophylaxis and treatment models of mouse influenza and was superior to the neuraminidase inhibitor, oseltamivir, including in delayed-start-to-treat experiments, with 100% survival at up to 96 h postinfection and partial survival in groups where the initiation of therapy was delayed up to 120 h postinfection. At different doses, VX-787 showed a 1-log to >5-log reduction in viral load (relative to vehicle controls) in mouse lungs. Overall, these favorable findings validate the PB2 subunit of the viral polymerase as a drug target for influenza therapy and support the continued development of VX-787 as a novel antiviral agent for the treatment of influenza infection.

Getting physical to fix pharma.

Powerful technologies allow the synthesis and testing of large numbers of new compounds, but the failure rate of pharmaceutical R&D remains very high. Greater understanding of the fundamental physical chemical behaviour of molecules could be the key to greatly enhancing the success rate of drug discovery.

The Discovery of VX-745: A Novel and Selective p38α Kinase Inhibitor.

The synthesis of novel, selective, orally active 2,5-disubstituted 6H-pyrimido[1,6-b]pyridazin-6-one p38α inhibitors is described. Application of structural information from enzyme-ligand complexes guided the selection of screening compounds, leading to the identification of a novel class of p38α inhibitors containing a previously unreported bicyclic heterocycle core. Advancing the SAR of this series led to the eventual discovery of 5-(2,6-dichlorophenyl)-2-(2,4-difluorophenylthio)-6H-pyrimido[1,6-b]pyridazin-6-one (VX-745). VX-745 displays excellent enzyme activity and selectivity, has a favorable pharmacokinetic profile, and demonstrates good in vivo activity in models of inflammation.

Kinase-likeness and kinase-privileged fragments: toward virtual polypharmacology.

Small molecule protein kinase inhibitors are widely employed as biological reagents and as leads in the design of drugs for a variety of diseases. We investigated the phenomenon of kinase-likeness, i.e., the propensity of ligands to inhibit protein kinases, in the context of kinase-specific substructural fragments. The frequency of occurrence of multiple structural fragments in kinase inhibitor libraries relative to nonkinase compounds has been analyzed. A combination of structural fragment counts, termed the "2-0" kinase-likeness rule, provides approximately 5-fold enrichment in kinase active compounds. This rule has been validated using in-house kinase counterscreening data and applied prospectively to uncover kinase activities in marketed drugs. In addition, the role of discriminating fragments in kinase recognition was interrogated using available structural data, providing an insight into their effect on inhibitor potency and selectivity. One of these fragments, bisarylaniline, has been characterized as a kinase-privileged fragment with specific binding preferences and a link to increased activity within kinases.

Inhibitors of hepatitis C virus NS3.4A protease. Effect of P4 capping groups on inhibitory potency and pharmacokinetics.

Reversible tetrapeptide-based compounds have been shown to effectively inhibit the hepatitis C virus NS3.4A protease. Inhibition of viral replicon RNA production in Huh-7 cells has also been demonstrated. We show herein that the inclusion of hydrogen bond donors on the P4 capping group of tetrapeptide-based inhibitors result in increased binding potency to the NS3.4A protease. The capping groups also impart significant effects on the pharmacokinetic profile of these inhibitors.

Toward a pharmacophore for kinase frequent hitters.

Small molecule protein kinase inhibitors are widely employed as biological reagents and as leads in the design of drugs for a variety of diseases. One of the hardest challenges in kinase inhibitor design is achieving target selectivity. By utilizing X-ray structural information for four promiscuous inhibitors, we propose a five-point pharmacophore for kinase frequent hitters, demonstrate its ability to discriminate between frequent hitters and selective ligands, and suggest a strategy for selective inhibitor design.

Inhibitors of hepatitis C virus NS3.4A protease. Part 3: P2 proline variants.

We recently described the identification of an optimized alpha-ketoamide warhead for our series of HCV NS3.4A inhibitors. We report herein a series of HCV protease inhibitors incorporating 3-alkyl-substituted prolines in P(2). These compounds show exceptional enzymatic and cellular potency given their relatively small size. The marked enhancement of activity of these 3-substituted proline derivatives relative to previously reported 4-hydroxyproline derivatives constitutes additional evidence for the importance of the S(2) binding pocket as the defining pharmacophore for inhibition of the NS3.4A enzyme.

Guiding molecules towards drug-likeness.

This review discusses computational methods for the prediction of drug-likeness. The coverage of published works include the assessment of historical practices of lead generation and optimization, surveys of the properties of known drugs and their constituent fragments and scaffolds, methods for delineating drug space, optimization techniques for simultaneously enhancing multiple properties and drug-like characteristics, similarity metrics and the application of more advanced pattern recognition algorithms for the prediction of drug-likeness. Areas which could be improved in this field are the scope of the datasets used to build models, the chemical interpretability of models, the use of multivariate optimization methods for drug design and the application of underappreciated statistical methods proven to work in other fields.

Prediction of 'drug-likeness'.

Recent developments in combinatorial chemistry and high-throughput screening have dramatically increased the scale on which drug discovery programs are carried out. Along with these advances has come a need for automated methods of determining which compounds from a library should be synthesized and screened. These methods range from simple counting schemes to sophisticated machine learning techniques such as neural networks. While many of these methods have performed well in validation studies, the field is still in its formative stage. This paper reviews a number of computational techniques for identifying drug-like molecules and examines challenges facing the field.