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

: 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 AtomNet® 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 AtomNet® 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

Prediction of body fat through body adiposity index and bioelectrical impedance analysis in a sample of physically active Mexican students

Objective: To compare the body fat percentage %BF predicted through the body adiposity index (BAI) BAI in a sample of physically active Mexican college students, using bioelectrical impedance analysis (BIA) as reference method. Methods: 78 volunteered university students (20.67 ± 1.69 yrs.) partake in study considered as highly active; the %BF determined by BIA was performed using Inbody 720; BAI was calculated by anthropometric assessment from hip and height measures calculated as follows: BAI=[hip circumference (cm)/height (m)1.5]–18. Pearson’s correlation coefficient was used to evaluate the association between BAI and %BF assessed by BIA. Results: The correlations of % BF between BIA and the estimated by BAI were r = 0.81, p < 0.001 in man and r = 0.69, p < 0.001 in women. Paired t-test in man showed a significant mean difference in %BF between methods (p = 0.001). The bias of the body adiposity was 5.77± 4.2 % (CI95% = 4.40 to 7.14), indicating that the body adiposity index method measured lower %BF than the bioelectrical impedance. Paired t-test in women did not show significant difference (p = 0.355). Lin’s concordance correlation coefficient was considered poor in man (ρc = 0.49) and women (ρc = 0.63), indicating than BAI underestimating %BF in relation to the BIA. Conclusion: In physically active Mexican college students, BAI presented low agreement with %BF measured by BIA; therefore, BAI is not recommended for %BF prediction in this sample studied. Objetivo: Comparar el porcentaje de grasa corporal % BF predicho por el índice de adiposidad corporal (BAI) y el análisis de impedancia bioeléctrica (BIA) en una muestra de estudiantes universitarios Mexicanos físicamente activos. Método: 78 estudiantes universitarios (Edad media= 20,67±1,69 años) voluntarios considerados físicamente activos mediante el cuestionario (IPAQ) participaron en el estudio; Para determinar el %GC con el método de referencia el AIB se realizó con el equipo Inbody 720; el IAC se determinó mediante valoración antropométrica de las medidas circunferencia de cadera y talla calculándose con la fórmula: IAC (%GC)=[circunferencia de cadera (cm)/talla(m)1,5]-18. Resultados: Las correlaciones de Pearson del %GC entre IAC y las estimada por AIB fue de r=0,81, p<0,001 en hombres y r=0.69, p<0.001 en mujeres. La prueba t-Student en hombres mostró diferencias significativas del %GC entre los métodos (p=0,001). El sesgo del %GC fue 5,77±4,2% (IC95%=4,40-7,14), lo que indica que la media del %GC por IAC fue inferior al AIB. La prueba t-Student en mujeres no mostró diferencias significativas (p=0,355). La concordancia del coeficiente de correlación de Lin se consideró pobre en hombres (ρc=0,49) y mujeres (ρc=0,63), indicando que el IAC subestima él %GC en relación con el AIB. Conclusión: En los estudiantes universitarios mexicanos físicamente activos evaluados, el IAC presentó baja concordancia del %GC medido por AIB; por lo anterior, el IAC no se recomienda para predecir el %GC en esta muestra estudiada