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+is+a+principled+approach+to+molecular+design+tasks.+In+this+paper+we+explain+three+pitfalls+of+BO+which+can+cause+poor+empirical+performance%3a+an+incorrect+prior+width%252C+over-smoothing%252C+and+inadequate+acquisition+function+maximization.+We+show+that+with+these+issues+addressed%252C+even+a+basic+BO+setup+is+able+to+achieve+the+highest+overall+performance+on+the+PMO+benchmark+for+molecule+design+(Gao+et+al+2022).+These+results+suggest+that+BO+may+benefit+from+more+attention+in+the+machine+learning+for+molecules+community.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.9e2cca9fddfb9bcfe48a762a29095c05%3frik%3dCDSuWNAeMZxemA%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Bayesian+Optimization&FORM=IRPRST&ck=8628C7577E848DDC9C4519313B268A0A&selectedIndex=43&itb=0)
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