Mol-CycleGAN - a generative model for molecular optimization

Designing a molecule with desired properties is one of the biggest challenges in drug development, as it requires optimization of chemical compound structures with respect to many complex properties. To augment the compound design process we introduce Mol-CycleGAN - a CycleGAN-based model that generates optimized compounds with high structural similarity to the original ones. Namely, given a molecule our model generates a structurally similar one with an optimized value of the considered property. We evaluate the performance of the model on selected optimization objectives related to structural properties (presence of halogen groups, number of aromatic rings) and to a physicochemical property (penalized logP). In the task of optimization of penalized logP of drug-like molecules our model significantly outperforms previous results

Ethical Challenges of Preexposure Prophylaxis for HIV

On July 16, 2012, emtricitabine/tenofovir (Truvada) became the first drug approved by the US Food and Drug Administration for preexposure prophylaxis (PrEP) of human immunodeficiency virus (HIV) for adults at high risk. While PrEP appears highly effective with consistent adherence, effective implementation poses ethical challenges for the medical and public health community. For PrEP users, it is necessary to maintain adherence, safe sex practices, and routine HIV testing and medical monitoring, to maximize benefits and reduce risks. On a population level, comparative cost-effectiveness should guide priority-setting, while safety measures must address drug resistance concerns without burdening patients\u27 access. Equitable distribution will require addressing the needs of underserved populations, women (for whom efficacy data are mixed) and people living in developing countries with high HIV incidence; meanwhile, it is necessary to consider the fair use of drugs for treatment vs. prevention and the appropriate design of new HIV prevention studies

De novo design of a homo-trimeric amantadine-binding protein.

The computational design of a symmetric protein homo-oligomer that binds a symmetry-matched small molecule larger than a metal ion has not yet been achieved. We used de novo protein design to create a homo-trimeric protein that binds the C3 symmetric small molecule drug amantadine with each protein monomer making identical interactions with each face of the small molecule. Solution NMR data show that the protein has regular three-fold symmetry and undergoes localized structural changes upon ligand binding. A high-resolution X-ray structure reveals a close overall match to the design model with the exception of water molecules in the amantadine binding site not included in the Rosetta design calculations, and a neutron structure provides experimental validation of the computationally designed hydrogen-bond networks. Exploration of approaches to generate a small molecule inducible homo-trimerization system based on the design highlight challenges that must be overcome to computationally design such systems

Nanotechnology and Drug Delivery Part 2: Nanostructures for Drug Delivery

This is the second part of a review on nanotechnology in general and particularly as it pertains to drug deliver. In the earlier paper (Part 1), nanotechnology in nature, its history as well as design and methodswere discussed. Its applications, benefits and risks were also outlined. In this paper (Part 2), various nanostructures employed in drug delivery, their methods of fabrication and challenges of nano drug delivery are reviewed. Nanotechnology is one approach to overcome challenges of conventional drugdelivery systems based on the development and fabrication of nanostructures. Some challenges associated with the technology as it relates to drug effectiveness, toxicity, stability, pharmacokinetics and drug regulatory control are discussed in this review. Clearly, nanotechnology is a welcomedevelopment that is set to transform drug delivery and drug supply chain management, if optimally developed

Composition of Biochemical Networks using Domain Knowledge

Graph composition has applications in a variety of practical applications. In drug development, for instance, in order to understand possible drug interactions, one has to merge known networks and examine topological variants arising from such composition. Similarly, the design of sensor nets may use existing network infrastructures, and the superposition of one network on another can help with network design and optimisation. The problem of network composition has not received much attention in algorithm and database research. Here, we work with biological networks encoded in Systems Biology Markup Language (SBML), based on XML syntax. We focus on XML merging and examine the algorithmic and performance challenges we encountered in our work and the possible solutions to the graph merge problem. We show that our XML graph merge solution performs well in practice and improves on the existing toolsets. This leads us into future work directions and the plan of research which will aim to implement graph merging primitives using domain knowledge to perform composition and decomposition on specific graphs in the biological domain

Artificial intelligence for dementia drug discovery and trials optimization

Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi-disciplinary approach can promote data-driven decision-making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation

Artificial intelligence for dementia drug discovery and trials optimization

Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi‐disciplinary approach can promote data‐driven decision‐making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation