10 research outputs found

    AI is a viable alternative to high throughput screening: a 318-target study

    Get PDF
    : High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNetĀ® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNetĀ® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

    DS_DISC767844 ā€“ Supplemental material for A Fragment Library Screening Approach to Identify Selective Inhibitors against an Essential Fungal Enzyme

    No full text
    <p>Supplemental material, DS_DISC767844 for A Fragment Library Screening Approach to Identify Selective Inhibitors against an Essential Fungal Enzyme by Gopal P. Dahal and Ronald E. Viola in SLAS Discovery</p

    Agricultural innovation and adaptation to climate change: empirical evidence from diverse agro-ecologies in South Asia

    Get PDF
    While impacts of climate change on agricultural systems have been widely researched, there is still limited understanding of what agricultural innovations have evolved over time in response to both climatic and non-climatic drivers. Although there has been some progress in formulating national adaptation policies and strategic planning in different countries of South Asia, research to identify local-level adaptive strategies and practices is still limited. Through eight case studies and a survey of 300 households in 15 locations in India, Nepal and Bangladesh, this paper generates empirical evidence on emerging agricultural innovations in contrasting socio-economic, geographical and agro-ecological contexts. The study demonstrates that several farm practices (innovations) have emerged in response to multiple drivers over time, with various forms of institutional and policy support, including incentives to reduce risks in the adoption of innovative practice. It further shows that there is still limited attempt to systematically mainstream adaptation innovations into local, regional and national government structures, policies and planning processes. The paper shows that the process of farm-level adaptation through innovation adoption forms an important avenue for agricultural adaptation in South Asia. A key implication of this finding is that there is a need for stronger collaborations between research institutions, extension systems, civil society and the private sector actors to enhance emerging adaptive innovations at the farm level
    corecore